Biomarkers and methods for assessing response to inflammatory disease therapy withdrawal

ABSTRACT

Provided herein are methods for assessing response to inflammatory disease therapy. The methods include placing a subject on a therapeutic regimen and subsequently performing an immunoassay to generate a score based on quantitative data for expression of biomarkers relating to inflammatory biomarkers. The methods further include recommending that the subject either remains on the therapeutic regimen, or is removed from the therapeutic regimen, based on the score.

RELATED APPLICATIONS

This application claims priority to International application numberPCT/US2016/054318, filed Sep. 29, 2016, which claims benefit to U.S.provisional application No. 62/234,468, filed Sep. 29, 2015, the entirecontents of which are hereby incorporated by reference.

BACKGROUND

This application is directed to the fields of bioinformatics andinflammatory and autoimmune diseases, with methods of assessing responseto inflammatory disease therapy withdrawal. RA is an example of aninflammatory disease, and is a chronic, systemic autoimmune disorder. Itis one of the most common systemic autoimmune diseases worldwide. Theimmune system of the RA subject targets the subject's joints as well asother organs including the lung, blood vessels and pericardium, leadingto inflammation of the joints (arthritis), widespread endothelialinflammation, and even destruction of joint tissue. Erosions and jointspace narrowing are largely irreversible and result in cumulativedisability.

The precise etiology of RA has not been established, but underlyingdisease pathogenesis is complex and includes inflammation and immunedysregulation. The precise mechanisms involved are different inindividual subjects, and can change in those subjects over time.Variables such as race, sex, genetics, hormones, and environmentalfactors can impact the development and severity of RA disease. Emergingdata are also beginning to reveal the characteristics of new RA subjectsubgroups and complex overlapping relationships with other autoimmunedisorders. Disease duration and level of inflammatory activity is alsoassociated with other comorbidities such as risk of lymphoma,extra-articular manifestations, and cardiovascular disease. See, e.g.,S. Banerjee et al., Am. J. Cardiol. 2008, 101(8):1201-1205; E. Baecklundet al., Arth. Rheum. 2006, 54(3):692-701; and, N. Goodson et al., Ann.Rheum. Dis. 2005, 64(11):1595-1601.

Traditional models for treating RA are based on the expectation thatcontrolling disease activity (i.e., inflammation) in an RA subjectshould slow or prevent disease progression, in terms of radiographicprogression, relapse, flare, tissue destruction, cartilage loss andjoint erosion. There is evidence, however, that disease activity anddisease progression can be uncoupled, and may not always functioncompletely in tandem. Indeed, different cell signaling pathways andmediators are involved in these two processes. See W. van den Berg etal., Arth. Rheum. 2005, 52:995-999. The uncoupling of diseaseprogression and disease activity is described in a number of RA clinicaltrials and animal studies. See, e.g., P E Lipsky et al., N. Engl. J.Med. 2003, 343:1594-602.; A K Brown et al., Arth. Rheum. 2006,54:3761-3773; and, A R Pettit et al., Am. J. Pathol. 2001, 159:1689-99.Recent advances in assessing inflammatory disease activity andprogression are described in US 2011/0137851, which is herebyincorporated by reference in its entirety.

To best study the uncoupling of disease progression and activity, e.g.,relapse, and to analyze the relationship between disease activity,treatment, and progression, RA subjects should be assessed frequentlyfor both disease activity and progression during therapy to determinethe necessity of remaining on said therapy.

There is a need to classify subjects by disease activity whenconsidering therapy withdrawal in order to ensure that each receivestreatment that is appropriate and optimized for that patient. Predictionof which inflammatory disease patients with low disease activity cansuccessfully discontinue therapy can improve the cost-effectiveness ofinflammatory disease management.

SUMMARY

The present teachings relate to biomarkers associated with inflammatorydisease, and with autoimmune disease, including RA, and methods of usingthe biomarkers to measure disease activity in a subject, and inparticular, in response to therapy.

In one embodiment, a method for recommending a therapeutic regimen in asubject having an autoimmune disorder is provided. The method comprisesa) administering a therapeutic regimen to the subject; b) performing animmunoassay on a sample from the subject to generate a score based on aset of quantitative data, wherein the set of quantitative data comprisesexpression data for at least four biomarkers, wherein the at least fourbiomarkers comprise at least four markers selected from chitinase 3-like1 (cartilage glycoprotein-39) (CHI3L1); C-reactive protein,pentraxin-related (CRP); epidermal growth factor (beta-urogastrone)(EGF); interleukin 6 (interferon, beta 2) (IL6); leptin (LEP); matrixmetallopeptidase 1 (interstitial collagenase) (MMP1); matrixmetallopeptidase 3 (stromelysin 1, progelatinase) (MMP3); resistin(RETN); serum amyloid A1 (SAA1); tumor necrosis factor receptorsuperfamily, member 1A (TNFRSF1A); vascular cell adhesion molecule 1(VCAM1); and, vascular endothelial growth factor A (VEGFA); and c)recommending i) withdrawal from the therapeutic regimen if the score islow or moderate; or ii) no withdrawal from the therapeutic regimen ifthe score is high. In an embodiment, the at least four biomarkerscomprise IL6, EGF, SAA1, and CRP. In an embodiment, the at least fourbiomarkers comprise IL6, EGF, VEGFA, LEP, SAA1, VCAM1, CRP, MMP1, MMP3,TNFRSF1A, RETN, and CHI3L1. In an embodiment, performance of theimmunoassay comprises: obtaining the sample, wherein the samplecomprises the protein markers; contacting the sample with a plurality ofdistinct reagents; generating a plurality of distinct complexes betweenthe reagents and markers; and detecting the complexes to generate thedata. In an embodiment, the immunoassay comprises a multiplex assay. Inan embodiment, the therapeutic regimen prevents radiographicprogression. In an embodiment, the therapeutic regimen prevents relapse.In an embodiment, the autoimmune disorder is an arthritic disorder. Inan embodiment, the arthritic disorder is rheumatoid arthritis. In anembodiment, the rheumatoid arthritis is early rheumatoid arthritis. Inan embodiment, the score is on a scale of 1-100, wherein is score is lowif the score is <30, wherein the score is moderate if the score is30-44, and wherein the score is high if the score is >44. In anembodiment, the therapeutic regimen is a disease modifyinganti-rheumatoid drug (DMARD). In an embodiment, the DMARD therapeuticregimen comprises one or more of MTX, sulfasalazine (SSZ), orhydroxychloroquine (HCQ). In an embodiment, the therapeutic regimen is abiologic therapeutic regimen. In an embodiment, the biologic therapeuticregimen comprises a TNF inhibitor. In an embodiment, the TNF inhibitoris infliximab. In an embodiment, the score is predictive of a clinicalassessment. In an embodiment, the clinical assessment is selected fromthe group consisting of: a DAS, a DAS28, a DAS28-CRP, a DAS28-ESR, aSharp score, a tender joint count (TJC), and a swollen joint count(SJC). In an embodiment, relapse is indicated by restarting therapy,escalation of therapy, or flare. In an embodiment, the flare isphysician-reported flare.

In another embodiment, a method for recommending a therapeutic regimenfor reducing or preventing radiographic progression (RP) or relapse in asubject having an autoimmune disorder is provided. The method comprisesa) administering a therapeutic regimen to the subject; b) performing animmunoassay on a sample from the subject to generate a score based on aset of quantitative data, wherein the set of quantitative data comprisesexpression data for at least four biomarkers, wherein the at least fourbiomarkers comprise at least four markers selected from chitinase 3-like1 (cartilage glycoprotein-39) (CHI3L1); C-reactive protein,pentraxin-related (CRP); epidermal growth factor (beta-urogastrone)(EGF); interleukin 6 (interferon, beta 2) (IL6); leptin (LEP); matrixmetallopeptidase 1 (interstitial collagenase) (MMP1); matrixmetallopeptidase 3 (stromelysin 1, progelatinase) (MMP3); resistin(RETN); serum amyloid A1 (SAA1); tumor necrosis factor receptorsuperfamily, member 1A (TNFRSF1A); vascular cell adhesion molecule 1(VCAM1); and, vascular endothelial growth factor A (VEGFA); and c)recommending i) withdrawal from the therapeutic regimen if the score islow or moderate; or ii) no withdrawal from the therapeutic regimen ifthe score is high. In an embodiment, the at least four biomarkerscomprise IL6, EGF, SAA1, and CRP. In an embodiment, the at least fourbiomarkers comprise IL6, EGF, VEGFA, LEP, SAA1, VCAM1, CRP, MMP1, MMP3,TNFRSF1A, RETN, and CHI3L1. In an embodiment, performance of theimmunoassay comprises: obtaining the sample, wherein the samplecomprises the protein markers; contacting the sample with a plurality ofdistinct reagents; generating a plurality of distinct complexes betweenthe reagents and markers; and detecting the complexes to generate thedata. In an embodiment, the immunoassay comprises a multiplex assay. Inan embodiment, the therapeutic regimen prevents radiographicprogression. In an embodiment, the therapeutic regimen prevents relapse.In an embodiment, the autoimmune disorder is an arthritic disorder. Inan embodiment, the arthritic disorder is rheumatoid arthritis. In anembodiment, the rheumatoid arthritis is early rheumatoid arthritis. Inan embodiment, the score is on a scale of 1-100, wherein is score is lowif the score is <30, wherein the score is moderate if the score is30-44, and wherein the score is high if the score is >44. In anembodiment, the therapeutic regimen is a disease modifyinganti-rheumatoid drug (DMARD). In an embodiment, the DMARD therapeuticregimen comprises one or more of MTX, sulfasalazine (SSZ), orhydroxychloroquine (HCQ). In an embodiment, the therapeutic regimen is abiologic therapeutic regimen. In an embodiment, the biologic therapeuticregimen comprises a TNF inhibitor. In an embodiment, the TNF inhibitoris infliximab. In an embodiment, the score is predictive of a clinicalassessment. In an embodiment, the clinical assessment is selected fromthe group consisting of: a DAS, a DAS28, a DAS28-CRP, a DAS28-ESR, aSharp score, a tender joint count (TJC), and a swollen joint count(SJC). In an embodiment, relapse is indicated by restarting therapy,escalation of therapy, or flare. In an embodiment, the flare isphysician-reported flare.

BRIEF DESCRIPTION OF THE DRAWINGS

The skilled artisan will understand that the drawings, described below,are for illustration purposes only. The drawings are not intended tolimit the scope of the present teachings in any way.

FIG. 1 depicts correlations of the MBDA algorithm predictions and CRPwith clinical assessments of disease activity, as described in Example1.

FIG. 2A illustrates Kaplan-Meier curves for relapse-free survival forpatients with low (<30, top line), moderate (30-44, middle line), andhigh (>40, bottom line) MBDA scores based on TNFi re-initiation.P-values for log-rank χ2 tests were 0.013, 0.002, and 0.004 forre-initiation, medication escalation, and physician-reported flare.

FIG. 2B illustrates Kaplan-Meier curves for relapse-free survival forpatients with low (<30, top line), moderate (30-44, middle line), andhigh (>40, bottom line) MBDA scores based on medication escalation.P-values for log-rank χ2 tests were 0.013, 0.002, and 0.004 forre-initiation, medication escalation, and physician-reported flare.

FIG. 2C illustrates Kaplan-Meier curves for relapse-free survival forpatients with low (<30, top line), moderate (30-44, middle line), andhigh (>40, bottom line) MBDA scores based on physician-reported flare.P-values for log-rank χ2 tests were 0.013, 0.002, and 0.004 forre-initiation, medication escalation, and physician-reported flare.

FIG. 3 illustrates a high-level block diagram of a computer (1600).Illustrated are at least one processor (1602) coupled to a chipset(1604). Also coupled to the chipset (1604) are a memory (1606), astorage device (1608), a keyboard (1610), a graphics adapter (1612), apointing device (1614), and a network adapter (1616). A display (1618)is coupled to the graphics adapter (1612). In one embodiment, thefunctionality of the chipset (1604) is provided by a memory controllerhub 1620) and an I/O controller hub (1622). In another embodiment, thememory (1606) is coupled directly to the processor (1602) instead of thechipset (1604). The storage device 1608 is any device capable of holdingdata, like a hard drive, compact disk read-only memory (CD-ROM), DVD, ora solid-state memory device. The memory (1606) holds instructions anddata used by the processor (1602). The pointing device (1614) may be amouse, track ball, or other type of pointing device, and is used incombination with the keyboard (1610) to input data into the computersystem (1600). The graphics adapter (1612) displays images and otherinformation on the display (1618). The network adapter (1616) couplesthe computer system (1600) to a local or wide area network.

DESCRIPTION OF VARIOUS EMBODIMENTS

These and other features of the present teachings will become moreapparent from the description herein. While the present teachings aredescribed in conjunction with various embodiments, it is not intendedthat the present teachings be limited to such embodiments. On thecontrary, the present teachings encompass various alternatives,modifications, and equivalents, as will be appreciated by those of skillin the art.

The present teachings relate generally to the identification ofbiomarkers associated with subjects having inflammatory and/orautoimmune diseases, for example RA, and that are useful in determiningor assessing disease activity, and in particular, in response toinflammatory disease therapy for recommending optimal therapy.

Most of the words used in this specification have the meaning that wouldbe attributed to those words by one skilled in the art. Wordsspecifically defined in the specification have the meaning provided inthe context of the present teachings as a whole, and as are typicallyunderstood by those skilled in the art. In the event that a conflictarises between an art-understood definition of a word or phrase and adefinition of the word or phrase as specifically taught in thisspecification, the specification shall control. It must be noted that,as used in the specification and the appended claims, the singular forms“a,” “an,” and “the” include plural referents unless the context clearlydictates otherwise.

Definitions

“Accuracy” refers to the degree that a measured or calculated valueconforms to its actual value. “Accuracy” in clinical testing relates tothe proportion of actual outcomes (true positives or true negatives,wherein a subject is correctly classified as having disease or ashealthy/normal, respectively) versus incorrectly classified outcomes(false positives or false negatives, wherein a subject is incorrectlyclassified as having disease or as healthy/normal, respectively). Otherand/or equivalent terms for “accuracy” can include, for example,“sensitivity,” “specificity,” “positive predictive value (PPV),” “theAUC,” “negative predictive value (NPV),” “likelihood,” and “odds ratio.”“Analytical accuracy,” in the context of the present teachings, refersto the repeatability and predictability of the measurement process.Analytical accuracy can be summarized in such measurements as, e.g.,coefficients of variation (CV), and tests of concordance and calibrationof the same samples or controls at different times or with differentassessors, users, equipment, and/or reagents. See, e.g., R. Vasan,Circulation 2006, 113(19):2335-2362 for a summary of considerations inevaluating new biomarkers.

The term “administering” as used herein refers to the placement of acomposition into a subject by a method or route that results in at leastpartial localization of the composition at a desired site such that adesired effect is produced. Routes of administration include both localand systemic administration. Generally, local administration results inmore of the composition being delivered to a specific location ascompared to the entire body of the subject, whereas, systemicadministration results in delivery to essentially the entire body of thesubject.

The term “algorithm” encompasses any formula, model, mathematicalequation, algorithmic, analytical or programmed process, or statisticaltechnique or classification analysis that takes one or more inputs orparameters, whether continuous or categorical, and calculates an outputvalue, index, index value or score. Examples of algorithms include butare not limited to ratios, sums, regression operators such as exponentsor coefficients, biomarker value transformations and normalizations(including, without limitation, normalization schemes that are based onclinical parameters such as age, gender, ethnicity, etc.), rules andguidelines, statistical classification models, and neural networkstrained on populations. Also of use in the context of biomarkers arelinear and non-linear equations and statistical classification analysesto determine the relationship between (a) levels of biomarkers detectedin a subject sample and (b) the level of the respective subject'sdisease activity.

The term “analyte” in the context of the present teachings can mean anysubstance to be measured, and can encompass biomarkers, markers, nucleicacids, electrolytes, metabolites, proteins, sugars, carbohydrates, fats,lipids, cytokines, chemokines, growth factors, proteins, peptides,nucleic acids, oligonucleotides, metabolites, mutations, variants,polymorphisms, modifications, fragments, subunits, degradation productsand other elements. For simplicity, standard gene symbols may be usedthroughout to refer not only to genes but also gene products/proteins,rather than using the standard protein symbol; e.g., APOA1 as usedherein can refer to the gene APOA1 and also the protein ApoAI. Ingeneral, hyphens are dropped from analyte names and symbols herein(IL-6=IL6).

To “analyze” includes determining a value or set of values associatedwith a sample by measurement of analyte levels in the sample. “Analyze”may further comprise and comparing the levels against constituent levelsin a sample or set of samples from the same subject or other subject(s).The biomarkers of the present teachings can be analyzed by any ofvarious conventional methods known in the art. Some such methods includebut are not limited to: measuring serum protein or sugar or metaboliteor other analyte level, measuring enzymatic activity, and measuring geneexpression.

The term “antibody” refers to any immunoglobulin-like molecule thatreversibly binds to another with the required selectivity. Thus, theterm includes any such molecule that is capable of selectively bindingto a biomarker of the present teachings. The term includes animmunoglobulin molecule capable of binding an epitope present on anantigen. The term is intended to encompass not only intactimmunoglobulin molecules, such as monoclonal and polyclonal antibodies,but also antibody isotypes, recombinant antibodies, bi-specificantibodies, humanized antibodies, chimeric antibodies, anti-idiopathic(anti-ID) antibodies, single-chain antibodies, Fab fragments, F(ab′)fragments, fusion protein antibody fragments, immunoglobulin fragments,F_(v) fragments, single chain F_(v) fragments, and chimeras comprisingan immunoglobulin sequence and any modifications of the foregoing thatcomprise an antigen recognition site of the required selectivity.

“Autoimmune disease” encompasses any disease, as defined herein,resulting from an immune response against substances and tissuesnormally present in the body. Examples of suspected or known autoimmunediseases include rheumatoid arthritis, early rheumatoid arthritis, axialspondyloarthritis, juvenile idiopathic arthritis, seronegativespondyloarthropathies, ankylosing spondylitis, psoriatic arthritis,antiphospholipid antibody syndrome, autoimmune hepatitis, Behçet'sdisease, bullous pemphigoid, coeliac disease, Crohn's disease,dermatomyositis, Goodpasture's syndrome, Graves' disease, Hashimoto'sdisease, idiopathic thrombocytopenic purpura, IgA nephropathy, Kawasakidisease, systemic lupus erythematosus, mixed connective tissue disease,multiple sclerosis, myasthenia gravis, polymyositis, primary biliarycirrhosis, psoriasis, scleroderma, Sjögren's syndrome, ulcerativecolitis, vasculitis, Wegener's granulomatosis, temporal arteritis,Takayasu's arteritis, Henoch-Schonlein purpura, leucocytoclasticvasculitis, polyarteritis nodosa, Churg-Strauss Syndrome, and mixedcryoglobulinemic vasculitis.

“Biomarker,” “biomarkers,” “marker” or “markers” in the context of thepresent teachings encompasses, without limitation, cytokines,chemokines, growth factors, proteins, peptides, nucleic acids,oligonucleotides, and metabolites, together with their relatedmetabolites, mutations, isoforms, variants, polymorphisms,modifications, fragments, subunits, degradation products, elements, andother analytes or sample-derived measures. Biomarkers can also includemutated proteins, mutated nucleic acids, variations in copy numbersand/or transcript variants. Biomarkers also encompass non-blood bornefactors and non-analyte physiological markers of health status, and/orother factors or markers not measured from samples (e.g., biologicalsamples such as bodily fluids), such as clinical parameters andtraditional factors for clinical assessments. Biomarkers can alsoinclude any indices that are calculated and/or created mathematically.Biomarkers can also include combinations of any one or more of theforegoing measurements, including temporal trends and differences.Biomarkers can include, but are not limited to, apolipoprotein A-I(APOA1); apolipoprotein C-III (APOC3); calprotectin; chemokine (C—Cmotif) ligand 22 (CCL22); chitinase 3-like 1 (cartilage glycoprotein-39)(CHI3L1, or YKL-40); C-reactive protein, pentraxin-related (CRP);epidermal growth factor (beta-urogastrone) (EGF); intercellular adhesionmolecule 1 (ICAM1); ICTP; interleukin 18 (interferon-gamma-inducingfactor) (IL18); interleukin 1, beta (IL1B); interleukin 6 receptor(IL6R); interleukin 8 (IL8); keratan sulfate, or KS; leptin (LEP);matrix metallopeptidase 1 (interstitial collagenase) (MMP1); matrixmetallopeptidase 3 (stromelysin 1, progelatinase) (MMP3); pyridinoline(cross-links formed in collagen, derived from three lysine residues),which may be referred to herein as PYD; resistin (RETN); serum amyloidA1 (SAA1); tumor necrosis factor receptor superfamily, member 1A(TNFRSF1A); vascular cell adhesion molecule 1 (VCAM1); and, vascularendothelial growth factor A (VEGFA).

A “clinical assessment,” or “clinical datapoint” or “clinical endpoint,”in the context of the present teachings can refer to a measure ofdisease activity or severity. A clinical assessment can include a score,a value, or a set of values that can be obtained from evaluation of asample (or population of samples) from a subject or subjects underdetermined conditions. A clinical assessment can also be a questionnairecompleted by a subject. A clinical assessment can also be predicted bybiomarkers and/or other parameters. One of skill in the art willrecognize that the clinical assessment for RA, as an example, cancomprise, without limitation, one or more of the following: DAS, DAS28,DAS28-ESR, DAS28-CRP, HAQ, mHAQ, MDHAQ, physician global assessment VAS,patient global assessment VAS, pain VAS, fatigue VAS, overall VAS, sleepVAS, SDAI, CDAI, RAPID3, RAPID4, RAPID5, ACR20, ACR50, ACR70, SF-36 (awell-validated measure of general health status), RA MM score (RAMRIS;or RA Mill scoring system), total Sharp score (TSS), van derHeijde-modified TSS, van der Heijde-modified Sharp score (or Sharp-vander Heijde score (SHS)), Larsen score, TJC, swollen joint count (SJC),CRP titer (or level), and ESR.

The term “clinical parameters” in the context of the present teachingsencompasses all measures of the health status of a subject. A clinicalparameter can be used to derive a clinical assessment of the subject'sdisease activity. Clinical parameters can include, without limitation:therapeutic regimen (including but not limited to DMARDs, whetherconventional or biologics, steroids, etc.), TJC, SJC, morning stiffness,arthritis of three or more joint areas, arthritis of hand joints,symmetric arthritis, rheumatoid nodules, radiographic changes and otherimaging, flare, gender/sex, age, race/ethnicity, disease duration,diastolic and systolic blood pressure, resting heart rate, height,weight, body-mass index, family history, CCP status (i.e., whethersubject is positive or negative for anti-CCP antibody), CCP titer, RFstatus, RF titer, ESR, CRP titer, menopausal status, and whether asmoker/non-smoker.

“Clinical assessment” and “clinical parameter” are not mutuallyexclusive terms. There may be overlap in members of the two categories.For example, CRP titer can be used as a clinical assessment of diseaseactivity; or, it can be used as a measure of the health status of asubject, and thus serve as a clinical parameter.

The term “computer” carries the meaning that is generally known in theart; that is, a machine for manipulating data according to a set ofinstructions. For illustration purposes only, FIG. 3 is a high-levelblock diagram of a computer (1600). As is known in the art, a “computer”can have different and/or other components than those shown in FIG. 3.In addition, the computer 1600 can lack certain illustrated components.Moreover, the storage device (1608) can be local and/or remote from thecomputer (1600) (such as embodied within a storage area network (SAN)).As is known in the art, the computer (1600) is adapted to executecomputer program modules for providing functionality described herein.As used herein, the term “module” refers to computer program logicutilized to provide the specified functionality. Thus, a module can beimplemented in hardware, firmware, and/or software. In one embodiment,program modules are stored on the storage device (1608), loaded into thememory (1606), and executed by the processor (1602). Embodiments of theentities described herein can include other and/or different modulesthan the ones described here. In addition, the functionality attributedto the modules can be performed by other or different modules in otherembodiments. Moreover, this description occasionally omits the term“module” for purposes of clarity and convenience.

The term “cytokine” in the present teachings refers to any substancesecreted by specific cells of the immune system that carries signalslocally between cells and thus has an effect on other cells. The term“cytokines” encompasses “growth factors.” “Chemokines” are alsocytokines. They are a subset of cytokines that are able to inducechemotaxis in cells; thus, they are also known as “chemotacticcytokines.”

“DAS” refers to the Disease Activity Score, a measure of the activity ofRA in a subject, well-known to those of skill in the art. See D. van derHeijde et al., Ann. Rheum. Dis. 1990, 49(11):916-920. “DAS” as usedherein refers to this particular Disease Activity Score. The “DAS28”involves the evaluation of 28 specific joints. It is a current standardwell-recognized in research and clinical practice. Because the DAS28 isa well-recognized standard, it is often simply referred to as “DAS.”Unless otherwise specified, “DAS” herein will encompass the DAS28. ADAS28 can be calculated for an RA subject according to the standard asoutlined at the das-score.nl website, maintained by the Department ofRheumatology of the University Medical Centre in Nijmegen, theNetherlands. The number of swollen joints, or swollen joint count out ofa total of 28 (SJC28), and tender joints, or tender joint count out of atotal of 28 (TJC28) in each subject is assessed. In some DAS28calculations the subject's general health (GH) is also a factor, and canbe measured on a 100 mm Visual Analogue Scale (VAS). GH may also bereferred to herein as PG or PGA, for “patient global health assessment”(or merely “patient global assessment”). A “patient global healthassessment VAS,” then, is GH measured on a Visual Analogue Scale.

“DAS28-CRP” (or “DAS28CRP”) is a DAS28 assessment calculated using CRPin place of ESR (see below). CRP is produced in the liver. Normallythere is little or no CRP circulating in an individual's blood serum—CRPis generally present in the body during episodes of acute inflammationor infection, so that a high or increasing amount of CRP in blood serumcan be associated with acute infection or inflammation. A blood serumlevel of CRP greater than 1 mg/dL is usually considered high. Mostinflammation and infections result in CRP levels greater than 10 mg/dL.The amount of CRP in subject sera can be quantified using, for example,the DSL-10-42100 ACTIVE® US C-Reactive Protein Enzyme-LinkedImmunosorbent Assay (ELISA), developed by Diagnostics SystemsLaboratories, Inc. (Webster, Tex.). CRP production is associated withradiological progression in RA. See M. Van Leeuwen et al., Br. J. Rheum.1993, 32(suppl.):9-13). CRP is thus considered an appropriatealternative to ESR in measuring RA disease activity. See R. Mallya etal., J. Rheum. 1982, 9(2):224-228, and F. Wolfe, J. Rheum. 1997,24:1477-1485.

The DAS28-CRP can be calculated according to either of the formulasbelow, with or without the GH factor, where “CRP” represents the amountof this protein present in a subject's blood serum in mg/L, “sqrt”represents the square root, and “ln” represents the natural logarithm:

DAS28-CRP with GH (orDAS28-CRP4)=(0.56*sqrt(TJC28)+0.28*sqrt(SJC28)+0.36*ln(CRP+1))+(0.014*GH)+0.96;or,  (a)

DAS28-CRP without GH (orDAS28-CRP3)=(0.56*sqrt(TJC28)+0.28*sqrt(SJC28)+0.36*ln(CRP+1))*1.10+1.15.  (b)

The “DAS28-ESR” is a DAS28 assessment wherein the ESR for each subjectis also measured (in mm/hour). The DAS28-ESR can be calculated accordingto the formula:

DAS28-ESR with GH (orDAS28-ESR4)=0.56*sqrt(TJC28)+0.28*sqrt(SJC28)+0.70*ln(ESR)+0.014*GH;or,  (a)

DAS28-ESR withoutGH=0.56*sqrt(TJC28)+0.28*sqrt(SJC28)+0.70*ln(ESR)*1.08+0.16.  (b)

Unless otherwise specified herein, the term “DAS28,” as used in thepresent teachings, can refer to a DAS28-ESR or DAS28-CRP, as obtained byany of the four formulas described above; or, DAS28 can refer to anotherreliable DAS28 formula as may be known in the art.

A “dataset” is a set of numerical values resulting from evaluation of asample (or population of samples) under a desired condition. The valuesof the dataset can be obtained, for example, by experimentally obtainingmeasures from a sample and constructing a dataset from thesemeasurements; or alternatively, by obtaining a dataset from a serviceprovider such as a laboratory, or from a database or a server on whichthe dataset has been stored.

A “difference” as used herein refers to an increase or decrease in themeasurable expression of a biomarker or panel of biomarkers as comparedto the measurable expression of the same biomarker or panel ofbiomarkers in a second samples.

The term “disease” in the context of the present teachings encompassesany disorder, condition, sickness, ailment, etc. that manifests in,e.g., a disordered or incorrectly functioning organ, part, structure, orsystem of the body, and results from, e.g., genetic or developmentalerrors, infection, poisons, nutritional deficiency or imbalance,toxicity, or unfavorable environmental factors.

A DMARD can be conventional or biologic. Examples of DMARDs that aregenerally considered conventional include, but are not limited to, MTX,azathioprine (AZA), bucillamine (BUC), chloroquine (CQ), ciclosporin(CSA, or cyclosporine, or cyclosporin), doxycycline (DOXY),hydroxychloroquine (HCQ), intramuscular gold (IM gold), leflunomide(LEF), levofloxacin (LEV), and sulfasalazine (SSZ). Examples of otherconventional DMARDs include, but are not limited to, folinic acid,D-pencillamine, gold auranofin, gold aurothioglucose, gold thiomalate,cyclophosphamide, and chlorambucil. Examples of biologic DMARDs (orbiologic drugs) include but are not limited to biological agents thattarget the tumor necrosis factor (TNF)-alpha molecules and the TNFinhibitors, such as infliximab, adalimumab, etanercept and golimumab.Other classes of biologic DMARDs include IL1 inhibitors such asanakinra, T-cell modulators such as abatacept, B-cell modulators such asrituximab, and IL6 inhibitors such as tocilizumab.

The term “flare” or “relapse” as used herein refers to the reappearanceof one or more symptoms of a disease state. For example, in the case ofrheumatoid arthritis, a reoccurrence can include the experience of oneor more swollen joints, morning stiffness, or joint tenderness. Flarecan be patient- or physician-reported.

An “immunoassay” as used herein refers to a biochemical assay that usesone or more antibodies to measure the presence or concentration of ananalyte or biomarker in a biological sample.

“Inflammatory disease” in the context of the present teachingsencompasses, without limitation, any disease, as defined herein,resulting from the biological response of vascular tissues to harmfulstimuli, including but not limited to such stimuli as pathogens, damagedcells, irritants, antigens and, in the case of autoimmune disease,substances and tissues normally present in the body. Non-limitingexamples of inflammatory disease include RA, ankylosing spondylitis,psoriatic arthritis, atherosclerosis, asthma, autoimmune diseases,chronic inflammation, chronic prostatitis, glomerulonephritis,hypersensitivities, inflammatory bowel diseases, pelvic inflammatorydisease, reperfusion injury, transplant rejection, and vasculitis.

“Interpretation function,” as used herein, means the transformation of aset of observed data into a meaningful determination of particularinterest; e.g., an interpretation function may be a predictive modelthat is created by utilizing one or more statistical algorithms totransform a dataset of observed biomarker data into a meaningfuldetermination of disease activity or the disease state of a subject.

“Measuring” or “measurement” in the context of the present teachingsrefers to determining the presence, absence, quantity, amount, oreffective amount of a substance in a clinical or subject-derived sample,including the concentration levels of such substances, or evaluating thevalues or categorization of a subject's clinical parameters.

“Performance” in the context of the present teachings relates to thequality and overall usefulness of, e.g., a model, algorithm, ordiagnostic or prognostic test. Factors to be considered in model or testperformance include, but are not limited to, the clinical and analyticalaccuracy of the test, use characteristics such as stability of reagentsand various components, ease of use of the model or test, health oreconomic value, and relative costs of various reagents and components ofthe test. Performing can mean the act of carrying out a function.

A “population” is any grouping of subjects of like specifiedcharacteristics. The grouping could be according to, for example butwithout limitation, clinical parameters, clinical assessments,therapeutic regimen, disease status (e.g. with disease or healthy),level of disease activity, etc. In the context of using the MBDA scorein comparing disease activity between populations, an aggregate valuecan be determined based on the observed MBDA scores of the subjects of apopulation; e.g., at particular timepoints in a longitudinal study. Theaggregate value can be based on, e.g., any mathematical or statisticalformula useful and known in the art for arriving at a meaningfulaggregate value from a collection of individual datapoints; e.g., mean,median, median of the mean, etc.

A “predictive model,” which term may be used synonymously herein with“multivariate model” or simply a “model,” is a mathematical constructdeveloped using a statistical algorithm or algorithms for classifyingsets of data. The term “predicting” refers to generating a value for adatapoint without actually performing the clinical diagnostic proceduresnormally or otherwise required to produce that datapoint; “predicting”as used in this modeling context should not be understood solely torefer to the power of a model to predict a particular outcome.Predictive models can provide an interpretation function; e.g., apredictive model can be created by utilizing one or more statisticalalgorithms or methods to transform a dataset of observed data into ameaningful determination of disease activity or the disease state of asubject. See Calculation of the MBDA score for some examples ofstatistical tools useful in model development.

A “prognosis” is a prediction as to the likely outcome of a disease.Prognostic estimates are useful in, e.g., determining an appropriatetherapeutic regimen for a subject.

A “quantitative dataset” or “quantitative data” as used in the presentteachings, refers to the data derived from, e.g., detection andcomposite measurements of expression of a plurality of biomarkers (i.e.,two or more) in a subject sample. The quantitative dataset can be usedto generate a score for the identification, monitoring and treatment ofdisease states, and in characterizing the biological condition of asubject. It is possible that different biomarkers will be detecteddepending on the disease state or physiological condition of interest.

“Recommending” as used herein refers to making a recommendation for atherapeutic regimen or excluding (i.e., not recommending) a certaintherapeutic regimen for a subject. Such a recommendation shall serveoptionally together with other information as a basis for a clinician toapply a certain therapeutic regimen for an individual subject.

A “sample” in the context of the present teachings refers to anybiological sample that is isolated from a subject. A sample can include,without limitation, a single cell or multiple cells, fragments of cells,an aliquot of body fluid, whole blood, platelets, serum, plasma, redblood cells, white blood cells or leucocytes, endothelial cells, tissuebiopsies, synovial fluid, lymphatic fluid, ascites fluid, andinterstitial or extracellular fluid. The term “sample” also encompassesthe fluid in spaces between cells, including gingival crevicular fluid,bone marrow, cerebrospinal fluid (CSF), saliva, mucous, sputum, semen,sweat, urine, or any other bodily fluids. “Blood sample” can refer towhole blood or any fraction thereof, including blood cells, red bloodcells, white blood cells or leucocytes, platelets, serum and plasma.Samples can be obtained from a subject by means including but notlimited to venipuncture, excretion, ejaculation, massage, biopsy, needleaspirate, lavage, scraping, surgical incision, or intervention or othermeans known in the art.

A “score” is a value or set of values selected so as to provide aquantitative measure of a variable or characteristic of a subject'scondition, and/or to discriminate, differentiate or otherwisecharacterize a subject's condition. The value(s) comprising the scorecan be based on, for example, quantitative data resulting in a measuredamount of one or more sample constituents obtained from the subject, orfrom clinical parameters, or from clinical assessments, or anycombination thereof. In certain embodiments the score can be derivedfrom a single constituent, parameter or assessment, while in otherembodiments the score is derived from multiple constituents, parametersand/or assessments. The score can be based upon or derived from aninterpretation function; e.g., an interpretation function derived from aparticular predictive model using any of various statistical algorithmsknown in the art. A “change in score” can refer to the absolute changein score, e.g. from one time point to the next, or the percent change inscore, or the change in the score per unit time (i.e., the rate of scorechange). A “score” as used herein can be used interchangeably with MBDAscore as defined below.

A “multi-biomarker disease activity index score,” “MBDA score,” orsimply “MBDA,” in the context of the present teachings, is a score thatuses quantitative data to provide a quantitative measure of inflammatorydisease activity or the state of inflammatory disease in a subject. Aset of data from particularly selected biomarkers, such as from thedisclosed set of biomarkers, is input into an interpretation functionaccording to the present teachings to derive the MBDA score. Theinterpretation function, in some embodiments, can be created frompredictive or multivariate modeling based on statistical algorithms.Input to the interpretation function can comprise the results of testingtwo or more of the disclosed set of biomarkers, alone or in combinationwith clinical parameters and/or clinical assessments, also describedherein. In some embodiments of the present teachings, the MBDA score isa quantitative measure of autoimmune disease activity. In someembodiments, the MBDA score is a quantitative measure of RA diseaseactivity. “MBDA” or “MBDA score” as used herein can refer to a VECTRA®DA score.

“Statistically significant” in the context of the present teachingsmeans an observed alteration is greater than what would be expected tooccur by chance alone (e.g., a “false positive”). Statisticalsignificance can be determined by any of various methods well-known inthe art. An example of a commonly used measure of statisticalsignificance is the p-value. The p-value represents the probability ofobtaining a given result equivalent to a particular datapoint, where thedatapoint is the result of random chance alone. A result is oftenconsidered highly significant (not random chance) at a p-value less thanor equal to 0.05.

A “subject” in the context of the present teachings is generally amammal. The subject can be a patient. The term “mammal” as used hereinincludes but is not limited to a human, non-human primate, dog, cat,mouse, rat, cow, horse, and pig. Mammals other than humans can beadvantageously used as subjects that represent animal models ofinflammation. A subject can be male or female. A subject can be one whohas been previously diagnosed or identified as having an inflammatorydisease. A subject can be one who has already undergone, or isundergoing, a therapeutic intervention for an inflammatory disease. Asubject can also be one who has not been previously diagnosed as havingan inflammatory disease; e.g., a subject can be one who exhibits one ormore symptoms or risk factors for an inflammatory condition, or asubject who does not exhibit symptoms or risk factors for aninflammatory condition, or a subject who is asymptomatic forinflammatory disease.

A “therapeutic regimen,” “therapy” or “treatment(s),” as describedherein, includes all clinical management of a subject and interventions,whether biological, chemical, physical, or a combination thereof,intended to sustain, ameliorate, improve, or otherwise alter thecondition of a subject. These terms may be used synonymously herein.Treatments include but are not limited to administration ofprophylactics or therapeutic compounds (including conventional DMARDs,biologic DMARDs, non-steroidal anti-inflammatory drugs (NSAID's) such asCOX-2 selective inhibitors, and corticosteroids), exercise regimens,physical therapy, dietary modification and/or supplementation, bariatricsurgical intervention, administration of pharmaceuticals and/oranti-inflammatories (prescription or over-the-counter), and any othertreatments known in the art as efficacious in preventing, delaying theonset of, or ameliorating disease. A “response to treatment” includes asubject's response to any of the above-described treatments, whetherbiological, chemical, physical, or a combination of the foregoing. A“treatment course” relates to the dosage, duration, extent, etc. of aparticular treatment or therapeutic regimen.

A “time point” as used herein refers to a manner of describing a time,which can be substantially described with a single point. A time pointmay also be described as a time range of a minimal unit which can bedetected. A time point can refer to a state of the aspect of a time or amanner of description of a certain period of time. Such a time point orrange can include, for example, an order of seconds, minutes to hours,or days.

Use of the Present Teachings in the Diagnosis, Prognosis, and Assessmentof Disease

In some embodiments of the present teachings, biomarkers can be used inthe derivation of a MBDA score, as described herein, which MBDA scorecan be used to provide diagnosis, prognosis and monitoring of diseasestate and/or disease activity in inflammatory disease and in autoimmunedisease. In certain embodiments, the MBDA score can be used to providediagnosis, prognosis and monitoring of disease state and/or diseaseactivity of RA in response to therapy. In some embodiments, the MBDAscore can be used to monitor therapy withdrawal.

Identifying the state of inflammatory disease in a subject allows for aprognosis of the disease, and thus for the informed selection of,initiation of, adjustment of or increasing or decreasing varioustherapeutic regimens in order to delay, reduce or prevent that subject'sprogression to a more advanced disease state. In some embodiments,therefore, subjects can be identified as having a particular level ofinflammatory disease activity and/or as being at a particular state ofdisease, based on the determination of their MBDA scores, and so can beselected to begin or accelerate treatment, as treatment is definedherein, to prevent or delay the further progression of inflammatorydisease. In other embodiments, subjects that are identified via theirMBDA scores as having a particular level of inflammatory diseaseactivity, and/or as being at a particular state of inflammatory disease,can be selected to have their treatment decreased or discontinued, whereimprovement or remission in the subject is seen.

In regards to the need for early and accurate diagnosis of RA, recentadvances in RA treatment provide a means for more profound diseasemanagement and optimal treatment of RA within the first months ofsymptom onset, which in turn result in significantly improved outcomes.See F. Wolfe, Arth. Rheum. 2000, 43(12):2751-2761; M. Matucci-Cerinic,Clin. Exp. Rheum. 2002, 20(4):443-444; and, V. Nell et. al., Lancet2005, 365(9455):199-200. Unfortunately, most subjects do not receiveoptimal treatment within this narrow window of opportunity, resulting inpoorer outcomes and irreversible joint damage, in part because of thelimits of current diagnostic laboratory tests. Numerous difficultiesexist in diagnosing RA subject. This is in part because at their earlystages, symptoms may not be fully differentiated. It is also becausediagnostic tests for RA were developed based on phenomenologicalfindings, not the biological basis of disease. In various embodiments ofthe present teachings, multi-biomarker algorithms can be derived fromthe disclosed set of biomarkers.

Rating Disease Activity

In some embodiments of the present teachings, the MBDA score, derived asdescribed herein, can be used to rate inflammatory disease activity;e.g., as high, medium or low. The score can be varied based on a set ofvalues chosen by the practitioner. For example, a score can be set suchthat a value is given a range from 0-100, and a difference between twoscores would be a value of at least one point. The practitioner can thenassign disease activity based on the values. For example, in someembodiments a score of 1 to 29 represents a low level of diseaseactivity, a score of 30 to 44 represents a moderate level of diseaseactivity, and a score of 45 to 100 represents a high level of diseaseactivity. The disease activity score can change based on the range ofthe score. For example a score of 1 to 58 can represent a low level ofdisease activity when a range of 0-200 is utilized. Differences can bedetermined based on the range of score possibilities. For example, ifusing a score range of 0-100, a small difference in scores can be adifference of about 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 points; a moderatedifference in scores can be a difference of about 4, 5, 6, 7, 8, 9, 10,11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28,29, or 30 points; and large differences can be a change in about 14, 15,16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 35, 40, 45,or 50 points. Thus, by way of example, a practitioner can define a smalldifference in scores as about ≤6 points, a moderate difference in scoresas about 7-20 points, and a large difference in scores as about >20points. The difference can be expressed by any unit, for example,percentage points. For example, a practitioner can define a smalldifference as about ≤6 percentage points, moderate difference as about7-20 percentage points, and a large difference as about >20 percentagepoints.

In some embodiments of the present teachings, autoimmune diseaseactivity can be so rated. In other embodiments, RA disease activity canbe so rated. Because the MBDA score correlates well with traditionalclinical assessments of inflammatory disease activity, e.g. in RA, inother embodiments of the present teachings bone damage itself in asubject or population, and thus disease progression, can be tracked viathe use and application of the MBDA score.

The MBDA score can be used for several purposes. On a subject-specificbasis, it provides a context for understanding the relative level ofdisease activity. The MBDA rating of disease activity can be used, e.g.,to guide the clinician in determining treatment, in setting a treatmentcourse, e.g., withdrawal from therapy, and/or to inform the clinicianthat the subject is in remission. Moreover, it provides a means to moreaccurately assess and document the qualitative level of disease activityin a subject. It is also useful from the perspective of assessingclinical differences among populations of subjects within a practice.For example, this tool can be used to assess the relative efficacy ofdifferent treatment modalities. Moreover, it is also useful from theperspective of assessing clinical differences among different practices.This would allow physicians to determine what global level of diseasecontrol is achieved by their colleagues, and/or for healthcaremanagement groups to compare their results among different practices forboth cost and comparative effectiveness. Because the MBDA scoredemonstrates strong association with established disease activityassessments, such as the DAS28, the MBDA score can provide aquantitative measure for monitoring the extent of subject diseaseactivity, and response to treatment.

Subject Screening

Certain embodiments of the present teachings can also be used to screensubject populations in any number of settings. For example, a healthmaintenance organization, public health entity or school health programcan screen a group of subjects to identify those requiringinterventions, as described above. Other embodiments of these teachingscan be used to collect disease activity data on one or more populationsof subjects, to identify subject disease status in the aggregate, inorder to, e.g., determine the effectiveness of the clinical managementof a population, or determine gaps in clinical management. Insurancecompanies (e.g., health, life, or disability) may request the screeningof applicants in the process of determining coverage for possibleintervention. Data collected in such population screens, particularlywhen tied to any clinical progression to conditions such as inflammatorydisease and RA, will be of value in the operations of, for example,health maintenance organizations, public health programs and insurancecompanies.

Such data arrays or collections can be stored in machine-readable mediaand used in any number of health-related data management systems toprovide improved healthcare services, cost-effective healthcare, andimproved insurance operation, among other things. See, e.g., U.S. PatentApplication No. 2002/0038227; U.S. Patent Application No. 2004/0122296;U.S. Patent Application No. 2004/0122297; and U.S. Pat. No. 5,018,067.Such systems can access the data directly from internal data storage orremotely from one or more data storage sites as further detailed herein.Thus, in a health-related data management system, wherein it isimportant to manage inflammatory disease progression for a population inorder to reduce disease-related employment productivity loss, disabilityand surgery, and thus reduce healthcare costs in the aggregate, variousembodiments of the present teachings provide an improvement comprisingthe use of a data array encompassing the biomarker measurements asdefined herein, and/or the resulting evaluation of disease status andactivity from those biomarker measurements.

Calculation of Scores

In some embodiments of the present teachings, inflammatory diseaseactivity in a subject is measured by: determining the levels ininflammatory disease subject serum of two or more biomarkers, thenapplying an interpretation function to transform the biomarker levelsinto a single MBDA score, which provides a quantitative measure ofinflammatory disease activity in the subject, correlating well withtraditional clinical assessments of inflammatory disease activity (e.g.,a DAS28 or CDAI score in RA), as is demonstrated in the Examples below.In some embodiments, the disease activity so measured relates to anautoimmune disease. In some embodiments, the disease activity someasured relates to RA. The biomarkers can include apolipoprotein A-I(APOA1); apolipoprotein C-III (APOC3); calprotectin; chemokine (C—Cmotif) ligand 22 (CCL22); chitinase 3-like 1 (cartilage glycoprotein-39)(CHI3L1, or YKL-40); C-reactive protein, pentraxin-related (CRP);epidermal growth factor (beta-urogastrone) (EGF); intercellular adhesionmolecule 1 (ICAM1); ICTP; interleukin 18 (interferon-gamma-inducingfactor) (IL18); interleukin 1, beta (IL1B); interleukin 6 receptor(IL6R); interleukin 8 (IL8); keratan sulfate, or KS; leptin (LEP);matrix metallopeptidase 1 (interstitial collagenase) (MMP1); matrixmetallopeptidase 3 (stromelysin 1, progelatinase) (MMP3); pyridinoline(cross-links formed in collagen, derived from three lysine residues),which may be referred to herein as PYD; resistin (RETN); serum amyloidA1 (SAA1); tumor necrosis factor receptor superfamily, member 1A(TNFRSF1A); vascular cell adhesion molecule 1 (VCAM1); and, vascularendothelial growth factor A (VEGFA). Selection of the biomarkers of thepresent invention is described in detail in US 2011/0137851.Calprotectin is a heteropolymer, comprising two protein subunits of genesymbols S100A8 and S100A9. ICTP is the carboxyterminal telopeptideregion of type I collagen, and is liberated during the degradation ofmature type I collagen. Type I collagen is present as fibers in tissue;in bone, the type I collagen molecules are crosslinked. The ICTP peptideis immunochemically intact in blood. (For the type I collagen gene, seeofficial symbol COL1A1, HUGO Gene Nomenclature Committee; also known as014; alpha 1 type I collagen; collagen alpha 1 chain type I; collagen ofskin, tendon and bone, alpha-1 chain; and, pro-alpha-1 collagen type 1).Keratan sulfate (KS, or keratosulfate) is not the product of a discretegene, but refers to any of several sulfated glycosaminoglycans. They aresynthesized in the central nervous system, and are found especially incartilage and bone. Keratan sulfates are large, highly hydratedmolecules, which in joints can act as a cushion to absorb mechanicalshock. In some embodiments, the biomarkers can comprise two, or three,or four, or five, or six, or seven, or eight, or nine, or ten, oreleven, or twelve of IL6, EGF, VEGFA, LEP, SAA1, VCAM1, CRP, MMP1, MMP3,TNFRSF1A, RETN, and CHI3L1. In some embodiments, the biomarkers cancomprise as least four biomarkers that comprise IL6, EGF, SAA1, and CRP.

In some embodiments, the interpretation function is based on apredictive model. Established statistical algorithms and methodswell-known in the art, useful as models or useful in designingpredictive models, can include but are not limited to: analysis ofvariants (ANOVA); Bayesian networks; boosting and Ada-boosting;bootstrap aggregating (or bagging) algorithms; decision treesclassification techniques, such as Classification and Regression Trees(CART), boosted CART, Random Forest (RF), Recursive Partitioning Trees(RPART), and others; Curds and Whey (CW); Curds and Whey-Lasso;dimension reduction methods, such as principal component analysis (PCA)and factor rotation or factor analysis; discriminant analysis, includingLinear Discriminant Analysis (LDA), Eigengene Linear DiscriminantAnalysis (ELDA), and quadratic discriminant analysis; DiscriminantFunction Analysis (DFA); factor rotation or factor analysis; geneticalgorithms; Hidden Markov Models; kernel based machine algorithms suchas kernel density estimation, kernel partial least squares algorithms,kernel matching pursuit algorithms, kernel Fisher's discriminateanalysis algorithms, and kernel principal components analysisalgorithms; linear regression and generalized linear models, includingor utilizing Forward Linear Stepwise Regression, Lasso (or LASSO)shrinkage and selection method, and Elastic Net regularization andselection method; glmnet (Lasso and Elastic Net-regularized generalizedlinear model); Logistic Regression (LogReg); meta-learner algorithms;nearest neighbor methods for classification or regression, e.g.Kth-nearest neighbor (KNN); non-linear regression or classificationalgorithms; neural networks; partial least square; rules basedclassifiers; shrunken centroids (SC); sliced inverse regression;Standard for the Exchange of Product model data, Application InterpretedConstructs (StepAIC); super principal component (SPC) regression; and,Support Vector Machines (SVM) and Recursive Support Vector Machines(RSVM), among others. Additionally, clustering algorithms as are knownin the art can be useful in determining subject sub-groups.

Logistic Regression is the traditional predictive modeling method ofchoice for dichotomous response variables; e.g., treatment 1 versustreatment 2. It can be used to model both linear and non-linear aspectsof the data variables and provides easily interpretable odds ratios.

Discriminant Function Analysis (DFA) uses a set of analytes as variables(roots) to discriminate between two or more naturally occurring groups.DFA is used to test analytes that are significantly different betweengroups. A forward step-wise DFA can be used to select a set of analytesthat maximally discriminate among the groups studied. Specifically, ateach step all variables can be reviewed to determine which willmaximally discriminate among groups. This information is then includedin a discriminative function, denoted a root, which is an equationconsisting of linear combinations of analyte concentrations for theprediction of group membership. The discriminatory potential of thefinal equation can be observed as a line plot of the root valuesobtained for each group. This approach identifies groups of analyteswhose changes in concentration levels can be used to delineate profiles,diagnose and assess therapeutic efficacy. The DFA model can also createan arbitrary score by which new subjects can be classified as either“healthy” or “diseased.” To facilitate the use of this score for themedical community the score can be rescaled so a value of 0 indicates ahealthy individual and scores greater than 0 indicate increasing diseaseactivity.

Classification and regression trees (CART) perform logical splits(if/then) of data to create a decision tree. All observations that fallin a given node are classified according to the most common outcome inthat node. CART results are easily interpretable—one follows a series ofif/then tree branches until a classification results.

Support vector machines (SVM) classify objects into two or more classes.Examples of classes include sets of treatment alternatives, sets ofdiagnostic alternatives, or sets of prognostic alternatives. Each objectis assigned to a class based on its similarity to (or distance from)objects in the training data set in which the correct class assignmentof each object is known. The measure of similarity of a new object tothe known objects is determined using support vectors, which define aregion in a potentially high dimensional space (>R6).

The process of bootstrap aggregating, or “bagging,” is computationallysimple. In the first step, a given dataset is randomly resampled aspecified number of times (e.g., thousands), effectively providing thatnumber of new datasets, which are referred to as “bootstrappedresamples” of data, each of which can then be used to build a model.Then, in the example of classification models, the class of every newobservation is predicted by the number of classification models createdin the first step. The final class decision is based upon a “majorityvote” of the classification models; i.e., a final classification call isdetermined by counting the number of times a new observation isclassified into a given group, and taking the majority classification(33%+ for a three-class system). In the example of logistical regressionmodels, if a logistical regression is bagged 1000 times, there will be1000 logistical models, and each will provide the probability of asample belonging to class 1 or 2.

Curds and Whey (CW) using ordinary least squares (OLS) is anotherpredictive modeling method. See L. Breiman and J H Friedman, J. Royal.Stat. Soc. B 1997, 59(1):3-54. This method takes advantage of thecorrelations between response variables to improve predictive accuracy,compared with the usual procedure of performing an individual regressionof each response variable on the common set of predictor variables X. InCW, Y=XB*S, where Y=(y_(kj)) with k for the k^(th) patient and j forj^(th) response (j=1 for TJC, j=2 for SJC, etc.), B is obtained usingOLS, and S is the shrinkage matrix computed from the canonicalcoordinate system. Another method is Curds and Whey and Lasso incombination (CW-Lasso). Instead of using OLS to obtain B, as in CW, hereLasso is used, and parameters are adjusted accordingly for the Lassoapproach.

Many of these techniques are useful either combined with a biomarkerselection technique (such as, for example, forward selection, backwardsselection, or stepwise selection), or for complete enumeration of allpotential panels of a given size, or genetic algorithms, or they canthemselves include biomarker selection methodologies in their owntechniques. These techniques can be coupled with information criteria,such as Akaike's Information Criterion (AIC), Bayes InformationCriterion (BIC), or cross-validation, to quantify the tradeoff betweenthe inclusion of additional biomarkers and model improvement, and tominimize overfit. The resulting predictive models can be validated inother studies, or cross-validated in the study they were originallytrained in, using such techniques as, for example, Leave-One-Out (LOO)and 10-Fold cross-validation (10-Fold CV).

One example of an interpretation function that provides a MBDA score,derived from a statistical modeling method as described above, is givenby the following function:

MBDA=b ₀ +b ₁*DAIMRK₁ ^(x) −b ₂*DAIMRK₂ ^(x) −b ₃*DAIMRK₃ ^(x) . . . −b_(n)*DAIMRK_(n) ^(x);

where MBDA is the MBDA score, b_(0-n) are constants, and DAIMRK_(1-n)^(x) are the serum concentrations to the x^(th) power of n differentbiomarkers selected from the biomarkers disclosed herein. MBDA scoresthus obtained for RA subjects with known clinical assessments (e.g.,DAS28 scores) can then be compared to those known assessments todetermine the level of correlation between the two assessments, andhence determine the accuracy of the MBDA score and its underlyingpredictive model. See Examples below for specific formulas andconstants.

More generally, the function can be described as:

MBDA=F(DAIMRK₁ ^(x), DAIMRK₂ ^(x), . . . , DAIMRK_(n) ^(x)) where MBDAis the MBDA score, F is the function, and DAIMRK_(1-n) ^(x) are theserum concentrations to the x^(th) power of n different biomarkersselected from the biomarkers disclosed herein. The function is describedin the following paragraph.

An interpretation function for providing a MBDA score can also bederived based on models built to predict components of a diseaseactivity assessment, such as DAS28-CRP, rather than predicting diseaseactivity entirely. See Example 1. An example of such a function is givenby the following, wherein biomarkers are used to provide improvedpredicted components of the DAS score:

MBDAscore=((0.56*sqrt(IPTJC))+(0.28*sqrt(IPSJC))+(0.14*PPGA)+(0.36*ln(CRP/10⁶+1))+0.96)*10.53+1;

IPTJC=Improved PTJC=max(0.1739*PTJC+0.7865*PSJC,0);

IPSJC=Improved PSJC=max(0.1734*PTJC+0.7839*PSJC,0);

PTJC=Prediction of Tender JointCount=−38.564+3.997*(SAA1)^(1/10)+17.331*(IL6)^(1/10)+4.665*(CHI3L1)^(1/10)−15.236*(EGF)^(1/10)+2.651*(TNFRSF1A)^(1/10)+2.641*(LEP)^(1/10)+4.026*(VEGFA)^(1/10)−1.47*(VCAM1)^(1/10);

PSJC=Prediction of Swollen JointCount=−25.444+4.051*(SAA1)^(1/10)+16.154*(IL6)^(1/10)−11.847*(EGF)^(1/10)+3.091*(CHI3L1)^(1/10)+0.353*(TNFRSF1A)^(1/10);

PPGA=Prediction of Patient GlobalAssessment=−13.489+5.474*(IL6)^(1/10)+0.486*(SAA1)^(1/10)+2.246*(MMP1)^(1/10)+1.684*(leptin)^(1/10)+4.14*(TNFRSF1A)^(1/10)+2.292*(VEGFA)^(1/10)−1.898*(EGF)^(1/10)+0.028*(MMP3)^(1/10)−2.892*(VCAM1)^(1/10)−0.506*(RETN)^(1/10)

in which serum levels x for all biomarkers but CRP are transformed asx^(1/10), units for all biomarkers are in pg/mL, and In is natural log,or log_(e).

Where CRP units are obtained in mg/L and other markers are pg/mL, MBDAscore=((0.56*sqrt(IPTJC))+(0.28*sqrt(IPSJC))+(0.14*(PPGA))+(0.36*ln(CRP+1))+0.96)*10.53+1.

The MBDA score can be further rounded and capped, in order to provide awhole number between 1 and 100, the scaled MBDA score. To accomplishthis, the immediately preceding function can be re-written:

scaled MBDAscore=round(max(min((0.56*sqrt(IPTJC)+(0.28*sqrt(IPSJC))+(0.14*(PPGA))+(0.36*ln(CRP+1)+0.96)*10.53+1,100),1)). Biomarker gene names provided in the above formulas representthe concentrations of those markers, and will depend on the types ofassays used.

In some embodiments of the present teachings, it is not required thatthe MBDA score be compared to any pre-determined “reference,” “normal,”“control,” “standard,” “healthy,” “pre-disease” or other like index, inorder for the MBDA score to provide a quantitative measure ofinflammatory disease activity in the subject.

In other embodiments of the present teachings, the amount of thebiomarker(s) can be measured in a sample and used to derive a MBDAscore, which MBDA score is then compared to a “normal” or “control”level or value, utilizing techniques such as, e.g., reference ordiscrimination limits or risk defining thresholds, in order to definecut-off points and/or abnormal values for inflammatory disease. Thenormal level then is the level of one or more biomarkers or combinedbiomarker indices typically found in a subject who is not suffering fromthe inflammatory disease under evaluation. Other terms for “normal” or“control” are, e.g., “reference,” “index,” “baseline,” “standard,”“healthy,” “pre-disease,” etc. Such normal levels can vary, based onwhether a biomarker is used alone or in a formula combined with otherbiomarkers to output a score. Alternatively, the normal level can be adatabase of biomarker patterns from previously tested subjects who didnot convert to the inflammatory disease under evaluation over aclinically relevant time period. Reference (normal, control) values canalso be derived from, e.g., a control subject or population whoseinflammatory disease activity level or state is known. In someembodiments of the present teachings, the reference value can be derivedfrom one or more subjects who have been exposed to treatment forinflammatory disease, or from one or more subjects who are at low riskof developing inflammatory disease, or from subjects who have shownimprovements in inflammatory disease activity factors (such as, e.g.,clinical parameters as defined herein) as a result of exposure totreatment. In some embodiments the reference value can be derived fromone or more subjects who have not been exposed to treatment; forexample, samples can be collected from (a) subjects who have receivedinitial treatment for inflammatory disease, and (b) subjects who havereceived subsequent treatment for inflammatory disease, to monitor theprogress of the treatment. A reference value can also be derived fromdisease activity algorithms or computed indices from population studies.

Measurement of Biomarkers

The quantity of one or more biomarkers of the present teachings can beindicated as a value. The value can be one or more numerical valuesresulting from the evaluation of a sample, and can be derived, e.g., bymeasuring level(s) of the biomarker(s) in a sample by an assay performedin a laboratory, or from dataset obtained from a provider such as alaboratory, or from a dataset stored on a server. Biomarker levels canbe measured using any of several techniques known in the art. Thepresent teachings encompass such techniques, and further include allsubject fasting and/or temporal-based sampling procedures for measuringbiomarkers.

The actual measurement of levels of the biomarkers can be determined atthe protein or nucleic acid level using any method known in the art.“Protein” detection comprises detection of full-length proteins, matureproteins, pre-proteins, polypeptides, isoforms, mutations, variants,post-translationally modified proteins and variants thereof, and can bedetected in any suitable manner. Levels of biomarkers can be determinedat the protein level, e.g., by measuring the serum levels of peptidesencoded by the gene products described herein, or by measuring theenzymatic activities of these protein biomarkers. Such methods arewell-known in the art and include, e.g., immunoassays based onantibodies to proteins encoded by the genes, aptamers or molecularimprints. Any biological material can be used for thedetection/quantification of the protein or its activity. Alternatively,a suitable method can be selected to determine the activity of proteinsencoded by the biomarker genes according to the activity of each proteinanalyzed. For biomarker proteins, polypeptides, isoforms, mutations, andvariants thereof known to have enzymatic activity, the activities can bedetermined in vitro using enzyme assays known in the art. Such assaysinclude, without limitation, protease assays, kinase assays, phosphataseassays, reductase assays, among many others. Modulation of the kineticsof enzyme activities can be determined by measuring the rate constant KMusing known algorithms, such as the Hill plot, Michaelis-Mentenequation, linear regression plots such as Lineweaver-Burk analysis, andScatchard plot.

Using sequence information provided by the public database entries forthe biomarker, expression of the biomarker can be detected and measuredusing techniques well-known to those of skill in the art. For example,nucleic acid sequences in the sequence databases that correspond tonucleic acids of biomarkers can be used to construct primers and probesfor detecting and/or measuring biomarker nucleic acids. These probes canbe used in, e.g., Northern or Southern blot hybridization analyses,ribonuclease protection assays, and/or methods that quantitativelyamplify specific nucleic acid sequences. As another example, sequencesfrom sequence databases can be used to construct primers forspecifically amplifying biomarker sequences in, e.g.,amplification-based detection and quantitation methods such asreverse-transcription based polymerase chain reaction (RT-PCR) and PCR.When alterations in gene expression are associated with geneamplification, nucleotide deletions, polymorphisms, post-translationalmodifications and/or mutations, sequence comparisons in test andreference populations can be made by comparing relative amounts of theexamined DNA sequences in the test and reference populations.

As an example, Northern hybridization analysis using probes whichspecifically recognize one or more of these sequences can be used todetermine gene expression. Alternatively, expression can be measuredusing RT-PCR; e.g., polynucleotide primers specific for thedifferentially expressed biomarker mRNA sequences reverse-transcribe themRNA into DNA, which is then amplified in PCR and can be visualized andquantified. Biomarker RNA can also be quantified using, for example,other target amplification methods, such as TMA, SDA, and NASBA, orsignal amplification methods (e.g., bDNA), and the like. Ribonucleaseprotection assays can also be used, using probes that specificallyrecognize one or more biomarker mRNA sequences, to determine geneexpression.

Alternatively, biomarker protein and nucleic acid metabolites can bemeasured. The term “metabolite” includes any chemical or biochemicalproduct of a metabolic process, such as any compound produced by theprocessing, cleavage or consumption of a biological molecule (e.g., aprotein, nucleic acid, carbohydrate, or lipid). Metabolites can bedetected in a variety of ways known to one of skill in the art,including the refractive index spectroscopy (RI), ultra-violetspectroscopy (UV), fluorescence analysis, radiochemical analysis,near-infrared spectroscopy (near-IR), nuclear magnetic resonancespectroscopy (NMR), light scattering analysis (LS), mass spectrometry,pyrolysis mass spectrometry, nephelometry, dispersive Ramanspectroscopy, gas chromatography combined with mass spectrometry, liquidchromatography combined with mass spectrometry, matrix-assisted laserdesorption ionization-time of flight (MALDI-TOF) combined with massspectrometry, ion spray spectroscopy combined with mass spectrometry,capillary electrophoresis, NMR and IR detection. See WO 04/056456 and WO04/088309, each of which is hereby incorporated by reference in itsentirety. In this regard, other biomarker analytes can be measured usingthe above-mentioned detection methods, or other methods known to theskilled artisan. For example, circulating calcium ions (Ca²⁺) can bedetected in a sample using fluorescent dyes such as the Fluo series,Fura-2A, Rhod-2, among others. Other biomarker metabolites can besimilarly detected using reagents that are specifically designed ortailored to detect such metabolites.

In some embodiments, a biomarker is detected by contacting a subjectsample with reagents, generating complexes of reagent and analyte, anddetecting the complexes. Examples of “reagents” include but are notlimited to nucleic acid primers and antibodies.

In some embodiments of the present teachings an antibody binding assayis used to detect a biomarker; e.g., a sample from the subject iscontacted with an antibody reagent that binds the biomarker analyte, areaction product (or complex) comprising the antibody reagent andanalyte is generated, and the presence (or absence) or amount of thecomplex is determined. The antibody reagent useful in detectingbiomarker analytes can be monoclonal, polyclonal, chimeric, recombinant,or a fragment of the foregoing, as discussed in detail above, and thestep of detecting the reaction product can be carried out with anysuitable immunoassay. The sample from the subject is typically abiological fluid as described above, and can be the same sample ofbiological fluid as is used to conduct the method described above.

Immunoassays carried out in accordance with the present teachings can behomogeneous assays or heterogeneous assays. Immunoassays carried out inaccordance with the present teachings can be multiplexed. In ahomogeneous assay the immunological reaction can involve the specificantibody (e.g., anti-biomarker protein antibody), a labeled analyte, andthe sample of interest. The label produces a signal, and the signalarising from the label becomes modified, directly or indirectly, uponbinding of the labeled analyte to the antibody. Both the immunologicalreaction of binding, and detection of the extent of binding, can becarried out in a homogeneous solution. Immunochemical labels which canbe employed include but are not limited to free radicals, radioisotopes,fluorescent dyes, enzymes, bacteriophages, and coenzymes. Immunoassaysinclude competition assays.

In a heterogeneous assay approach, the reagents can be the sample ofinterest, an antibody, and a reagent for producing a detectable signal.Samples as described above can be used. The antibody can be immobilizedon a support, such as a bead (such as protein A and protein G agarosebeads), plate or slide, and contacted with the sample suspected ofcontaining the biomarker in liquid phase. The support is separated fromthe liquid phase, and either the support phase or the liquid phase isexamined using methods known in the art for detecting signal. The signalis related to the presence of the analyte in the sample. Methods forproducing a detectable signal include but are not limited to the use ofradioactive labels, fluorescent labels, or enzyme labels. For example,if the antigen to be detected contains a second binding site, anantibody which binds to that site can be conjugated to a detectable(signal-generating) group and added to the liquid phase reactionsolution before the separation step. The presence of the detectablegroup on the solid support indicates the presence of the biomarker inthe test sample. Examples of suitable immunoassays include but are notlimited to oligonucleotides, immunoblotting, immunoprecipitation,immunofluorescence methods, chemiluminescence methods,electrochemiluminescence (ECL), and/or enzyme-linked immunoassays(ELISA).

Those skilled in the art will be familiar with numerous specificimmunoassay formats and variations thereof which can be useful forcarrying out the method disclosed herein. See, e.g., E. Maggio,Enzyme-Immunoassay (1980), CRC Press, Inc., Boca Raton, Fla. See alsoU.S. Pat. No. 4,727,022 to C. Skold et al., titled “Novel Methods forModulating Ligand-Receptor Interactions and their Application”; U.S.Pat. No. 4,659,678 to G C Forrest et al., titled “Immunoassay ofAntigens”; U.S. Pat. No. 4,376,110 to G S David et al., titled“Immunometric Assays Using Monoclonal Antibodies”; U.S. Pat. No.4,275,149 to D. Litman et al., titled “Macromolecular EnvironmentControl in Specific Receptor Assays”; U.S. Pat. No. 4,233,402 to E.Maggio et al., titled “Reagents and Method Employing Channeling”; and,U.S. Pat. No. 4,230,797 to R. Boguslaski et al., titled “HeterogenousSpecific Binding Assay Employing a Coenzyme as Label.”

Antibodies can be conjugated to a solid support suitable for adiagnostic assay (e.g., beads such as protein A or protein G agarose,microspheres, plates, slides or wells formed from materials such aslatex or polystyrene) in accordance with known techniques, such aspassive binding. Antibodies as described herein can likewise beconjugated to detectable labels or groups such as radiolabels (e.g.,35S, 125I, 131I), enzyme labels (e.g., horseradish peroxidase, alkalinephosphatase), and fluorescent labels (e.g., fluorescein, Alexa, greenfluorescent protein, rhodamine) in accordance with known techniques.

Antibodies may also be useful for detecting post-translationalmodifications of biomarkers. Examples of post-translationalmodifications include, but are not limited to tyrosine phosphorylation,threonine phosphorylation, serine phosphorylation, citrullination andglycosylation (e.g., O-GlcNAc). Such antibodies specifically detect thephosphorylated amino acids in a protein or proteins of interest, and canbe used in the immunoblotting, immunofluorescence, and ELISA assaysdescribed herein. These antibodies are well-known to those skilled inthe art, and commercially available. Post-translational modificationscan also be determined using metastable ions in reflectormatrix-assisted laser desorption ionization-time of flight massspectrometry (MALDI-TOF). See U. Wirth et al., Proteomics 2002,2(10):1445-1451.

Therapeutic Regimens

The present invention provides methods of recommending therapeuticregimens, including withdrawal from therapeutic regimens, following thedetermination of differences in expression of the biomarkers disclosedherein. Measuring scores derived from expression levels of thebiomarkers disclosed herein over a period of time can provide aclinician with a dynamic picture of a subject's biological state. Theseembodiments of the present teachings thus will provide subject-specificbiological information, which will be informative for therapy decisionand will facilitate therapy response monitoring, and should result inmore rapid and more optimized treatment, better control of diseaseactivity, and an increase in the proportion of subjects achievingremission.

Treatment strategies for autoimmune disorders are confounded by the factthat some autoimmune disorders, such as RA, is a classification given toa group of subjects with a diverse array of related symptoms. Thissuggests that certain subtypes of RA are driven by specific cell type orcytokine. As a likely consequence, no single therapy has proven optimalfor treatment. Given the increasing numbers of therapeutic optionsavailable for RA, the need for an individually tailored treatmentdirected by immunological prognostic factors of treatment outcome isimperative. In various embodiments of the present teachings, abiomarker-derived algorithm can be used to quantify therapy response inRA subjects. For patients with early RA (eRA), methotrexate (MTX) issometimes recommended as a first-line treatment and in non-respondersboth the addition of conventional non-biological disease modifyinganti-rheumatic drug therapy (e.g., triple DMARD therapy) and ofbiological (e.g., anti-TNF) therapy are supported by data.Identification of patients with a higher likelihood of responding to oneor the other of these options would lead to more personalized medicineand increased effectiveness of therapeutic regimens, which is a primaryobjective of this invention.

Reference Standards for Treatment

In many embodiments, the levels of one or more analyte biomarkers or thelevels of a specific panel of analyte biomarkers in a sample arecompared to a reference standard (“reference standard” or “referencelevel”) in order to direct treatment decisions. Expression levels of theone or more biomarkers can be combined into a score, which can representdisease activity. The reference standard used for any embodimentdisclosed herein may comprise average, mean, or median levels of the oneor more analyte biomarkers or the levels of the specific panel ofanalyte biomarkers in a control population. The reference standard mayfurther include an earlier time point for the same subject. For example,a reference standard may include a first time point, and the levels ofthe one or more analyte biomarkers can be examined again at second,third, fourth, fifth, sixth time points, etc. Any time point earlierthan any particular time point can be considered a reference standard.The reference standard may additionally comprise cutoff values or anyother statistical attribute of the control population, or earlier timepoints of the same subject, such as a standard deviation from the meanlevels of the one or more analyte biomarkers or the levels of thespecific panel of analyte biomarkers. In some embodiments, the controlpopulation may comprise healthy individuals or the same subject prior tothe administration of any therapy.

In some embodiments, a score may be obtained from the reference timepoint, and a different score may be obtained from a later time point. Afirst time point can be when an initial therapeutic regimen is begun. Afirst time point can also be when a first immunoassay is performed. Atime point can be hours, days, months, years, etc. In some embodiments,a time point is one month. In some embodiments, a time point is twomonths. In some embodiments, a time point is three months. In someembodiments, a time point is four months. In some embodiments, a timepoint is five months. In some embodiments, a time point is six months.In some embodiments, a time point is seven months. In some embodiments,a time point is eight months. In some embodiments, a time point is ninemonths. In some embodiments, a time point is ten months. In someembodiments, a time point is eleven months. In some embodiments, a timepoint is twelve months. In some embodiments, a time point is two years.In some embodiments, a time point is three years. In some embodiments, atime point is four years. In some embodiments, a time point is fiveyears. In some embodiments, a time point is ten years.

A difference in the score can be interpreted as a decrease in diseaseactivity. For example, a lower score can indicate a lower level ofdisease activity. In these circumstances a second score having a lowerscore than the reference score, or first score, means that the subject'sdisease activity has been lowered (improved) between the first andsecond time periods. Alternatively, a higher score can indicate a lowerlevel of disease activity. In these circumstances, a second score havinga higher score than the reference score, or first score, also means thatthe subject's disease activity has improved between the first and secondtime periods.

A difference in the score can also be interpreted as an increase indisease activity. For example, lower score can indicate a higher levelof disease activity. In these circumstances a second score having alower score than the reference score, or first score, means that thesubject's disease activity has been increased (worsened) between thefirst and second time periods. Alternatively, a higher score canindicate a higher level of disease activity. In these circumstances, asecond score having a higher score than the reference score, or firstscore, also means that the subject's disease activity has worsenedbetween the first and second time periods.

The differences can be variable. For example, when a difference in thescore is interpreted as a decrease in disease activity, a largedifference can mean a greater decrease in disease activity than a loweror moderate difference. Alternatively, when a difference in the score isinterpreted as an increase in disease activity, a large difference canmean a greater increase in disease activity than a lower or moderatedifference.

Reference Therapy for Treatment

In some embodiments, a patient is treated more or less aggressively thana reference therapy based on the difference of scores. In someembodiments, a therapy is withdrawn or maintained than a referencetherapy based on the difference of scores. A reference therapy is anytherapy that is the standard of care for the autoimmune disorder. Thestandard of care can vary temporally and geographically, and a skilledperson can easily determine the appropriate standard of care byconsulting the relevant medical literature.

In some embodiments, a more aggressive therapy than the standard therapycomprises beginning treatment earlier than in the standard therapy. Insome embodiments, a more aggressive therapy than the standard therapycomprises administering additional treatments than in the standardtherapy. In some embodiments, a more aggressive therapy than thestandard therapy comprises treating on an accelerated schedule comparedto the standard therapy. In some embodiments, a more aggressive therapythan the standard therapy comprises administering additional treatmentsnot called for in the standard therapy.

In some embodiments, a less aggressive therapy than the standard therapycomprises delaying treatment relative to the standard therapy. In someembodiments, a less aggressive therapy than the standard therapycomprises administering less treatment than in the standard therapy. Insome embodiments, a less aggressive therapy than the standard therapycomprises administering treatment on a decelerated schedule compared tothe standard therapy. In some embodiments, a less aggressive therapythan the standard therapy comprises withdrawing therapy. In someembodiments, a less aggressive therapy than the standard therapycomprises administering no treatment.

Therapy Withdrawal

In some embodiments, prediction of autoimmune disease patients, inparticular RA patients, who can successfully withdrawal from ordiscontinue therapy, can be based on an MBDA score. The therapy to beconsidered for withdrawal can be any therapy as described herein.

In some embodiments, a high MBDA score as described herein at baselinecan be an independent predictor of disease progression within a certainperiod of time following discontinuation of therapy. In someembodiments, a moderate MBDA score as described herein at baseline canbe an independent predictor of disease progression within a certainperiod of time following discontinuation of therapy. In someembodiments, a low MBDA score as described herein at baseline can be anindependent predictor of disease progression within a certain period oftime following discontinuation of therapy.

In some embodiments, the disease progression for therapy withdrawal isrelapse. Relapse can be indicated by restarting therapy, escalation oftherapy, or flare as defined herein. In some embodiments, the diseaseprogression for therapy withdrawal is radiographic progression. In otherembodiments, the disease progression for therapy withdrawal is any otherconsequence of autoimmune diseases, in particular RA, known in the art.

The period of time in which an MBDA score can predict diseaseprogression can be one, two, three, four, five, six, seven, eight, nine,ten, eleven, or twelve months. The period of time in which an MBDA scorecan predict disease progression can also be one, two, three, four, five,ten, fifteen, twenty, twenty-five, or more years.

In some embodiments, a low MBDA score as described herein can result inthe recommendation that therapy is withdrawn. In some embodiments, a lowor moderate MBDA score as described herein can result in therecommendation that therapy is withdrawn. In some embodiments, amoderate or high MBDA score as described herein can result in therecommendation that therapy is not withdrawn. In some embodiments, ahigh MBDA score as described herein can result in the recommendationthat therapy is not withdrawn.

In some embodiments, the therapy can be partial withdrawn. In someembodiments, the therapy can be withdrawn partially in relation to theMBDA score. For purposes of illustration, a low MBDA score as describedherein can result in the recommendation that therapy is completelywithdrawn; whereas a moderate MBDA score as described herein can resultin the recommendation that therapy is reduced, for example, a lower dosecan be recommended. Thus, in some embodiments, dose can be adjustedbased on the MBDA score.

Treatment of Autoimmune Disorders

In one embodiment, the practitioner adjusts the therapy based on acomparison between difference scores. In one embodiment, thepractitioner adjusts the therapy by selecting and administering adifferent drug. In one embodiment, the practitioner adjusts the therapyby selecting and administering a different combination of drugs. In oneembodiment, the practitioner adjusts the therapy by adjusting drugdosage. In one embodiment, the practitioner adjusts the therapy byadjusting dose schedule. In one embodiment, the practitioner adjusts thetherapy by adjusting length of therapy. In one embodiment, thepractitioner adjusts the therapy by selecting and administering adifferent drug combination and adjusting drug dosage. In one embodiment,the practitioner adjusts the therapy by selecting and administering adifferent drug combination and adjusting dose schedule. In oneembodiment, the practitioner adjusts the therapy by selecting andadministering a different drug combination and adjusting length oftherapy. In one embodiment, the practitioner adjusts the therapy byadjusting drug dosage and dose schedule. In one embodiment, thepractitioner adjusts the therapy by adjusting drug dosage and adjustinglength of therapy. In one embodiment, the practitioner adjusts thetherapy by adjusting dose schedule and adjusting length of therapy. Inone embodiment, the practitioner adjusts the therapy by selecting andadministering a different drug, adjusting drug dosage, and adjustingdose schedule. In one embodiment, the practitioner adjusts the therapyby selecting and administering a different drug, adjusting drug dosage,and adjusting length of therapy. In one embodiment, the practitioneradjusts the therapy by selecting and administering a different drug,adjusting dose schedule, and adjusting length of therapy. In oneembodiment, the practitioner adjusts the therapy by adjusting drugdosage, adjusting dose schedule, and adjusting length of therapy. In oneembodiment, the practitioner adjusts the therapy by selecting andadministering a different drug, adjusting drug dosage, adjusting doseschedule, and adjusting length of therapy.

In one embodiment a less aggressive therapy comprises delayingtreatment. In one embodiment a less aggressive therapy comprisesselecting and administering less potent drugs. In one embodiment a lessaggressive therapy comprises decreasing the frequency treatment. In oneembodiment a less aggressive therapy comprises shortening length oftherapy. In one embodiment, less aggressive therapy comprises selectingand administering less potent drugs and decreasing drug dosage. In oneembodiment, less aggressive therapy comprises selecting andadministering less potent drugs and decelerating dose schedule. In oneembodiment, less aggressive therapy comprises selecting andadministering less potent drugs and shortening length of therapy. In oneembodiment, less aggressive therapy comprises decreasing drug dosage anddecelerating dose schedule. In one embodiment, less aggressive therapycomprises decreasing drug dosage and shortening length of therapy. Inone embodiment, less aggressive therapy comprises decelerating doseschedule and shortening length of therapy. In one embodiment, lessaggressive therapy comprises selecting and administering less potentdrugs, decreasing drug dosage, and decelerating dose schedule. In oneembodiment, less aggressive therapy comprises selecting andadministering less potent drugs, decreasing drug dosage, and shorteninglength of therapy. In one embodiment, less aggressive therapy comprisesselecting and administering less potent drugs, decelerating doseschedule, and shortening length of therapy. In one embodiment, lessaggressive therapy comprises decreasing drug dosage, decelerating doseschedule, and shortening length of therapy. In one embodiment, lessaggressive therapy comprises selecting and administering less potentdrugs, decreasing drug dosage, decelerating dose schedule, andshortening length of therapy. In some embodiments, a less aggressivetherapy comprises administering only non-drug-based therapies.

In another aspect of the present application, treatment comprises a moreaggressive therapy than a reference therapy. In one embodiment a moreaggressive therapy comprises increased length of therapy. In oneembodiment a more aggressive therapy comprises increased frequency ofthe dose schedule. In one embodiment, more aggressive therapy comprisesselecting and administering more potent drugs and increasing drugdosage. In one embodiment, more aggressive therapy comprises selectingand administering more potent drugs and accelerating dose schedule. Inone embodiment, more aggressive therapy comprises selecting andadministering more potent drugs and increasing length of therapy. In oneembodiment, more aggressive therapy comprises increasing drug dosage andaccelerating dose schedule. In one embodiment, more aggressive therapycomprises increasing drug dosage and increasing length of therapy. Inone embodiment, more aggressive therapy comprises accelerating doseschedule and increasing length of therapy. In one embodiment, moreaggressive therapy comprises selecting and administering more potentdrugs, increasing drug dosage, and accelerating dose schedule. In oneembodiment, more aggressive therapy comprises selecting andadministering more potent drugs, increasing drug dosage, and increasinglength of therapy. In one embodiment, more aggressive therapy comprisesselecting and administering more potent drugs, accelerating doseschedule, and increasing length of therapy. In one embodiment, moreaggressive therapy comprises increasing drug dosage, accelerating doseschedule, and increasing length of therapy. In one embodiment, moreaggressive therapy comprises selecting and administering more potentdrugs, increasing drug dosage, accelerating dose schedule, andincreasing length of therapy. In some embodiments, a more aggressivetherapy comprises administering a combination of drug-based andnon-drug-based therapies.

Therapies can be conventional or biologic. Examples of therapies, suchas disease modifying anti-rheumatic drugs (DMARD) that are generallyconsidered conventional include, but are not limited to, MTX,azathioprine (AZA), bucillamine (BUC), chloroquine (CQ), ciclosporin(CSA, or cyclosporine, or cyclosporin), doxycycline (DOXY),hydroxychloroquine (HCQ), intramuscular gold (IM gold), leflunomide(LEF), levofloxacin (LEV), and sulfasalazine (SSZ). Examples of otherconventional therapies include, but are not limited to, folinic acid,D-pencillamine, gold auranofin, gold aurothioglucose, gold thiomalate,cyclophosphamide, and chlorambucil. Examples of biologic drugs caninclude but are not limited to biological agents that target the tumornecrosis factor (TNF)-alpha molecules and the TNF inhibitors, such asinfliximab, adalimumab, etanercept and golimumab. Other classes ofbiologic drugs include IL1 inhibitors such as anakinra, T-cellmodulators such as abatacept, B-cell modulators such as rituximab, andIL6 inhibitors such as tocilizumab.

To identify additional therapeutics or drugs that are appropriate for aspecific subject, a test sample from the subject can also be exposed toa therapeutic agent or a drug, and the level of one or more biomarkerscan be determined. The level of one or more biomarkers can be comparedto sample derived from the subject before and after treatment orexposure to a therapeutic agent or a drug, or can be compared to samplesderived from one or more subjects who have shown improvements ininflammatory disease state or activity (e.g., clinical parameters ortraditional laboratory risk factors) as a result of such treatment orexposure.

Clinical Assessments of the Present Teachings

In some embodiments of the present teachings, MBDA scores are tailoredto the population, endpoints or clinical assessment, and/or use that isintended. For example, a MBDA score can be used to assess subjects forprimary prevention and diagnosis, and for secondary prevention andmanagement. For the primary assessment, the MBDA score can be used forprediction and risk stratification for future conditions or diseasesequelae, for the diagnosis of inflammatory disease, for the prognosisof disease activity and rate of change, and for indications for futurediagnosis and therapeutic regimens. For secondary prevention andclinical management, the MBDA score can be used for prognosis and riskstratification. The MBDA score can be used for clinical decisionsupport, such as determining whether to defer intervention or treatment,to recommend preventive check-ups for at-risk patients, to recommendincreased visit frequency, to recommend increased testing, and torecommend intervention. The MBDA score can also be useful fortherapeutic selection, determining response to treatment, adjustment anddosing of treatment, monitoring ongoing therapeutic efficiency, andindication for change in therapeutic regimen.

In some embodiments of the present teachings, the MBDA score can be usedto aid in the diagnosis of inflammatory disease, and in thedetermination of the severity of inflammatory disease. The MBDA scorecan also be used for determining the future status of intervention suchas, for example in RA, determining the prognosis of future joint erosionwith or without treatment. Certain embodiments of the present teachingscan be tailored to a specific treatment or a combination of treatments.X-ray is currently considered the gold standard for assessment ofdisease progression, but it has limited capabilities since subjects mayhave long periods of active symptomatic disease while radiographs remainnormal or show only nonspecific changes. Conversely, subjects who seemto have quiescent disease (subclinical disease) may slowly progress overtime, undetected clinically until significant radiographic progressionhas occurred. If subjects with a high likelihood of disease progressioncould be identified in advance, the opportunity for early aggressivetreatment could result in much more effective disease outcomes. See,e.g., M. Weinblatt et al., N. Engl. J. Med. 1999, 340:253-259.

Systems for Implementing Disease Activity Tests

Tests for measuring disease activity according to various embodiments ofthe present teachings can be implemented on a variety of systemstypically used for obtaining test results, such as results fromimmunological or nucleic acid detection assays. Such systems maycomprise modules that automate sample preparation, that automate testingsuch as measuring biomarker levels, that facilitate testing of multiplesamples, and/or are programmed to assay the same test or different testson each sample. In some embodiments, the testing system comprises one ormore of a sample preparation module, a clinical chemistry module, and animmunoassay module on one platform. Testing systems are typicallydesigned such that they also comprise modules to collect, store, andtrack results, such as by connecting to and utilizing a databaseresiding on hardware. Examples of these modules include physical andelectronic data storage devices as are well-known in the art, such as ahard drive, flash memory, and magnetic tape. Test systems also generallycomprise a module for reporting and/or visualizing results. Someexamples of reporting modules include a visible display or graphicaluser interface, links to a database, a printer, etc. See sectionMachine-readable storage medium, below.

One embodiment of the present invention comprises a system fordetermining the inflammatory disease activity of a subject. In someembodiments, the system employs a module for applying a formula to aninput comprising the measured levels of biomarkers in a panel, asdescribed herein, and outputting a score. In some embodiments, themeasured biomarker levels are test results, which serve as inputs to acomputer that is programmed to apply the formula. The system maycomprise other inputs in addition to or in combination with biomarkerresults in order to derive an output score; e.g., one or more clinicalparameters such as therapeutic regimen, TJC, SJC, morning stiffness,arthritis of three or more joint areas, arthritis of hand joints,symmetric arthritis, rheumatoid nodules, radiographic changes and otherimaging, gender/sex, age, race/ethnicity, disease duration, height,weight, body-mass index, family history, CCP status, RF status, ESR,smoker/non-smoker, etc. In some embodiments the system can apply aformula to biomarker level inputs, and then output a disease activityscore that can then be analyzed in conjunction with other inputs such asother clinical parameters. In other embodiments, the system is designedto apply a formula to the biomarker and non-biomarker inputs (such asclinical parameters) together, and then report a composite outputdisease activity index.

A number of testing systems are presently available that could be usedto implement various embodiments of the present teachings. See, forexample, the ARCHITECT series of integrated immunochemistrysystems—high-throughput, automated, clinical chemistry analyzers(ARCHITECT is a registered trademark of Abbott Laboratories, AbbottPark, Ill. 60064). See C. Wilson et al., “Clinical Chemistry AnalyzerSub-System Level Performance,” American Association for ClinicalChemistry Annual Meeting, Chicago, Ill., Jul. 23-27, 2006; and, H JKisner, “Product development: the making of the Abbott ARCHITECT,” Clin.Lab. Manage. Rev. 1997 November-December, 11(6):419-21; A. Ognibene etal., “A new modular chemiluminescence immunoassay analyzer evaluated,”Clin. Chem. Lab. Med. 2000 March, 38(3):251-60; J W Park et al.,“Three-year experience in using total laboratory automation system,”Southeast Asian J. Trop. Med. Public Health 2002, 33 Suppl 2:68-73; D.Pauli et al., “The Abbott Architect c8000: analytical performance andproductivity characteristics of a new analyzer applied to generalchemistry testing,” Clin. Lab. 2005, 51(1-2):31-41.

Another testing system useful for embodiments of the present teachingsis the VITROS system (VITROS is a registered trademark of Johnson &Johnson Corp., New Brunswick, N.J.)—an apparatus for chemistry analysisthat is used to generate test results from blood and other body fluidsfor laboratories and clinics. Another testing system is the DIMENSIONsystem (DIMENSION is a registered trademark of Dade Behring Inc.,Deerfield Ill.)—a system for the analysis of body fluids, comprisingcomputer software and hardware for operating the analyzers, andanalyzing the data generated by the analyzers.

The testing required for various embodiments of the present teachings,e.g. measuring biomarker levels, can be performed by laboratories suchas those certified under the Clinical Laboratory Improvement Amendments(42 U.S.C. Section 263(a)), or by laboratories certified under any otherfederal or state law, or the law of any other country, state or provincethat governs the operation of laboratories that analyze samples forclinical purposes. Such laboratories include, for example, LaboratoryCorporation of America, 358 South Main Street, Burlington, N.C. 27215(corporate headquarters); Quest Diagnostics, 3 Giralda Farms, Madison,N.J. 07940 (corporate headquarters); and other reference and clinicalchemistry laboratories.

Kits

Other embodiments of the present teachings comprise biomarker detectionreagents packaged together in the form of a kit for conducting any ofthe assays of the present teachings. In certain embodiments, the kitscomprise oligonucleotides that specifically identify one or morebiomarker nucleic acids based on homology and/or complementarity withbiomarker nucleic acids. The oligonucleotide sequences may correspond tofragments of the biomarker nucleic acids. For example, theoligonucleotides can be more than 200, 200, 150, 100, 50, 25, 10, orfewer than 10 nucleotides in length. In other embodiments, the kitscomprise antibodies to proteins encoded by the biomarker nucleic acids.The kits of the present teachings can also comprise aptamers. The kitcan contain in separate containers a nucleic acid or antibody (theantibody either bound to a solid matrix, or packaged separately withreagents for binding to a matrix), control formulations (positive and/ornegative), and/or a detectable label, such as but not limited tofluorescein, green fluorescent protein, rhodamine, cyanine dyes, Alexadyes, luciferase, and radiolabels, among others. Instructions forcarrying out the assay, including, optionally, instructions forgenerating a MBDA score, can be included in the kit; e.g., written,tape, VCR, or CD-ROM. The assay can for example be in the form of aNorthern hybridization or a sandwich ELISA as known in the art.

In some embodiments of the present teachings, biomarker detectionreagents can be immobilized on a solid matrix, such as a porous strip,to form at least one biomarker detection site. In some embodiments, themeasurement or detection region of the porous strip can include aplurality of sites containing a nucleic acid. In some embodiments, thetest strip can also contain sites for negative and/or positive controls.Alternatively, control sites can be located on a separate strip from thetest strip. Optionally, the different detection sites can containdifferent amounts of immobilized nucleic acids, e.g., a higher amount inthe first detection site and lesser amounts in subsequent sites. Uponthe addition of test sample, the number of sites displaying a detectablesignal provides a quantitative indication of the amount of biomarkerpresent in the sample. The detection sites can be configured in anysuitably detectable shape and can be, e.g., in the shape of a bar or dotspanning the width of a test strip.

In other embodiments of the present teachings, the kit can contain anucleic acid substrate array comprising one or more nucleic acidsequences. The nucleic acids on the array specifically identify one ormore nucleic acid sequences represented by the MBDA markers. In variousembodiments, the expression of one or more of the sequences representedby the MBDA markers can be identified by virtue of binding to the array.In some embodiments the substrate array can be on a solid substrate,such as what is known as a “chip.” See, e.g., U.S. Pat. No. 5,744,305.In some embodiments the substrate array can be a solution array; e.g.,xMAP (Luminex, Austin, Tex.), Cyvera (Illumina, San Diego, Calif.),RayBio Antibody Arrays (RayBiotech, Inc., Norcross, Ga.), CellCard(Vitra Bioscience, Mountain View, Calif.) and Quantum Dots' Mosaic(Invitrogen, Carlsbad, Calif.).

Machine-Readable Storage Medium

A machine-readable storage medium can comprise, for example, a datastorage material that is encoded with machine-readable data or dataarrays. The data and machine-readable storage medium are capable ofbeing used for a variety of purposes, when using a machine programmedwith instructions for using said data. Such purposes include, withoutlimitation, storing, accessing and manipulating information relating tothe inflammatory disease activity of a subject or population over time,or disease activity in response to inflammatory disease treatment, orfor drug discovery for inflammatory disease, etc. Data comprisingmeasurements of the biomarkers of the present teachings, and/or theevaluation of disease activity or disease state from these biomarkers,can be implemented in computer programs that are executing onprogrammable computers, which comprise a processor, a data storagesystem, one or more input devices, one or more output devices, etc.Program code can be applied to the input data to perform the functionsdescribed herein, and to generate output information. This outputinformation can then be applied to one or more output devices, accordingto methods well-known in the art. The computer can be, for example, apersonal computer, a microcomputer, or a workstation of conventionaldesign.

The computer programs can be implemented in a high-level procedural orobject-oriented programming language, to communicate with a computersystem such as for example, the computer system illustrated in FIG. 3.The programs can also be implemented in machine or assembly language.The programming language can also be a compiled or interpreted language.Each computer program can be stored on storage media or a device such asROM, magnetic diskette, etc., and can be readable by a programmablecomputer for configuring and operating the computer when the storagemedia or device is read by the computer to perform the describedprocedures. Any health-related data management systems of the presentteachings can be considered to be implemented as a computer-readablestorage medium, configured with a computer program, where the storagemedium causes a computer to operate in a specific manner to performvarious functions, as described herein.

The biomarkers disclosed herein can be used to generate a “subjectbiomarker profile” taken from subjects who have inflammatory disease.The subject biomarker profiles can then be compared to a referencebiomarker profile, in order to diagnose or identify subjects withinflammatory disease, to monitor the progression or rate of progressionof inflammatory disease, or to monitor the effectiveness of treatmentfor inflammatory disease. The biomarker profiles, reference and subject,of embodiments of the present teachings can be contained in amachine-readable medium, such as analog tapes like those readable by aCD-ROM or USB flash media, among others. Such machine-readable media canalso contain additional test results, such as measurements of clinicalparameters and clinical assessments. The machine-readable media can alsocomprise subject information; e.g., the subject's medical or familyhistory. The machine-readable media can also contain informationrelating to other disease activity algorithms and computed scores orindices, such as those described herein.

EXAMPLES

Aspects of the present teachings can be further understood in light ofthe following examples, which should not be construed as limiting thescope of the present teachings in any way.

The practice of the present teachings employ, unless otherwiseindicated, conventional methods of protein chemistry, biochemistry,recombinant DNA techniques and pharmacology, within the skill of theart. Such techniques are explained fully in the literature. See, e.g.,T. Creighton, Proteins: Structures and Molecular Properties, 1993, W.Freeman and Co.; A. Lehninger, Biochemistry, Worth Publishers, Inc.(current addition); J. Sambrook et al., Molecular Cloning: A LaboratoryManual, 2nd Edition, 1989; Methods In Enzymology, S. Colowick and N.Kaplan, eds., Academic Press, Inc.; Remington's Pharmaceutical Sciences,18th Edition, 1990, Mack Publishing Company, Easton, Pa.; Carey andSundberg, Advanced Organic Chemistry, Vols. A and B, 3rd Edition, 1992,Plenum Press.

The practice of the present teachings also employ, unless otherwiseindicated, conventional methods of statistical analysis, within theskill of the art. Such techniques are explained fully in the literature.See, e.g., J. Little and D. Rubin, Statistical Analysis with MissingData, 2nd Edition 2002, John Wiley and Sons, Inc., NJ; M. Pepe, TheStatistical Evaluation of Medical Tests for Classification andPrediction (Oxford Statistical Science Series) 2003, Oxford UniversityPress, Oxford, UK; X. Zhoue et al., Statistical Methods in DiagnosticMedicine 2002, John Wiley and Sons, Inc., NJ; T. Hastie et. al, TheElements of Statistical Learning: Data Mining, Inference, andPrediction, Second Edition 2009, Springer, NY; W. Cooley and P. Lohnes,Multivariate procedures for the behavioral science 1962, John Wiley andSons, Inc. NY; E. Jackson, A User's Guide to Principal Components 2003,John Wiley and Sons, Inc., NY.

Example 1: Deriving an MBDA Score

This example demonstrates a method of deriving a Multi-Biomarker DiseaseActivity (MBDA) score, based on a dataset of quantitative data forbiomarkers. In this example, a MBDA score is determined from thebiomarker data using an interpretation function that is based on a setof predictive models, where each predictive model is predictive of acomponent of the DAS28-CRP, in this example TJC, SJC and patient globalhealth assessment (GHA). Deriving an MBDA score as described in thisexample is described in detail in U.S. Ser. No. 12/905,984, hereinincorporated by reference in its entirety.

MBDA Algorithm Development and Evaluation Training Data

A MBDA algorithm was trained using clinical and biomarker data forpatients in the InFoRM and BRASS studies. The InFoRM study (Index ForRheumatoid Arthritis Measurement) is a multi-center observational studyof the North American RA population. The patients used in algorithmtraining were recruited between April and September 2009 from 25 sitesin the U.S. and Canada. Inclusion criteria were: age>18 years with adiagnosis of RA made by a board-certified rheumatologist. Patientsconcurrently enrolled in therapeutic drug trials involving a biologicagent and a placebo arm were excluded. The study includes three visitsfor each patient, each with clinical data and biological samplecollection, at approximately three-month intervals.

BRASS is an observational study of approximately 1,000 RA patientsreceiving care at the RB Brigham Arthritis and Musculoskeletal DiseasesClinical Research Center, at the Brigham and Women's Hospital, Boston,Mass. Inclusion criteria were: age>18 years with a diagnosis of RA madeby a board-certified rheumatologist. The study includes annual visitswith clinical data and biological sample collection, and patientquestionnaires between visits.

The first data set used in training consisted of visit 1 data for 512InFoRM patients. The 512 patient visits were chosen to have clinicalcharacteristics representative of the entire study population at thetime of selection, and also to have been evaluated by a limited numberof joint assessors. The number of joint assessors was limited to 12 sothat assessor-specific biases could be evaluated and taken into accountin algorithm development. The average age of these patients was 58.9years (range 20-91), and 76% were female. The mean SJC and TJC were 4.28and 5.49, respectively.

Assays for 25 candidate biomarkers were run on serum from the 512 InFoRMvisits. Those biomarkers were SAA1, IL6, TNFRSF1A, VEGFA, PYD, MMP1,ICAM1, calprotectin, CHI3L1 (YKL40), MMP3, EGF, IL1RA, VCAM1, LEP, RETN,CRP, IL8, APOA1, APOC3, CCL22, IL1B, IL6R, IL18, keratan sulfate andICTP. All the biomarker assays were run on the Meso Scale Discovery(MSD®) platform. See Example 1 of US 2011/0137851 for specifics ofbiomarker assay development and evaluation.

The biomarkers were prioritized based on (1) univariate association withdisease activity, (2) contribution to multivariate models for diseaseactivity, and (3) assay performance.

The assays for 20 candidate biomarkers were run in a second set ofpatient samples, comprising 167 samples from BRASS and 29 from InFoRM.These 20 candidate biomarkers were SAA1, IL6, TNFRSF1A, VEGFA, PYD,MMP1, ICAM1, calprotectin, YKL40, MMP3, EGF, IL1RA, VCAM1, leptin,resistin, CRP, IL8, CCL22, IL1B and IL6R. The samples were selected toenrich the overall training data for extremes of disease activity, whilealso having good representation of patients with moderate diseaseactivity. Enriching for extreme phenotypes can result in improvedalgorithm training, as long as the resulting training population stillfully represents the types of patients on which the algorithm will usedin independent validation and intended use populations. The 167 BRASSsamples were intended to represent similar numbers of patients with low,moderate and high disease activity. The 29 InFoRM samples were selectedto represent patients with high disease activity, since low and moderateactivity patients were already well represented by the first 512training samples.

Data Analysis

Prior to statistical analyses, all assay data were reviewed forpass/fail criteria on parameters including inter-assay CV, intra-assayCV, percent of samples within the measurable range of the calibrationcurve, and four serum process controls within the range of thecalibration curve. The biomarker values that were not in the measurablerange of the calibration curves were marked as missing data, and imputedwith the lowest/highest detected value across all the samples within agiven biomarker assay during the data export process. If the intra-assayCV of the biomarker concentration, computed from two replicates, wasgreater than 30%, it was also considered missing and excluded fromunivariate analyses. For multivariate analysis, individual biomarkerswere excluded entirely if more than 20% of their data values weremissing, and other missing data were imputed by the KNN algorithm (withk=5 nearest neighbors). In the data used for algorithm training, nobiomarkers were excluded from multivariate analysis because they all hadless than 20% missing values. Concentration values were transformed asχ0.1 prior to further analysis in order to make the distribution ofvalues for each biomarker more normal. This transformation has a similareffect to log transformation but avoids the generation of negativevalues. The transformed, imputed biomarker dataset is denoted asX_(n×m), where X is the protein data from n markers and m samples.

In univariate analysis, the Pearson correlations between the levels ofeach biomarker and disease activity measures including DAS28-CRP4,DAS28-ESR4, SJC, TJC, GHA, SDAI and CDAI were calculated.

In multivariate analysis, statistical models were developed by fivedifferent regression methods. In the first regression method (1),forward stepwise ordinary least square regression, the equation Y=Xβ+εapplies, where Y is the column vector with observed values, 0 is thevector of coefficients, and ε is the residuals. The forward selectionbegins with no variables in the model. Then, given a collection ofpredictors X, the one having the largest absolute correlation with theresponse Y is selected and a simple linear regression of Y on X1 isperformed. The residual vector is now orthogonal to X1, and is taken tobe the new response variable. The other predictors are then projectedorthogonally to X1 and the forward selection process is repeated.

In the second method (2), Lasso is used to prioritize biomarkers (basedon R² values) and to obtain a Lasso model. The “lasso” in this modelminimizes the residual sum of squares, subject to the sum of theabsolute value of the coefficients being less than a constant. Thismethod produces interpretable models and exhibits the stability of ridgeregression. See R. Tibshirani, J. Royal Stat. Soc. B 1996,58(1):267-288.

In the third method (3), the Elastic Net, mixtures of Lasso and ridgepenalties are applied. It encourages a grouping effect, where stronglycorrelated predictors segregate together, either tending to be in or outof the model together. See T. Zou, J. Royal Stat. Soc. B 2005,67(2):301-320. For each of the above three methods, the marker selectedat each step is recorded.

The fourth method (4) is Multivariate Response with Curds and Whey (CW)using ordinary least squares (OLS). See L. Breiman and J H Friedman, J.Royal. Stat. Soc. B 1997, 59(1):3-54. This method takes advantage of thecorrelations between the response variables (e.g., components of DAS) toimprove predictive accuracy, compared with the usual procedure ofperforming an individual regression of each response variable on thecommon set of predictor variables X. In CW, Y=XB*S, where Y=(y_(kj))with k for the k^(th) patient and j for j^(th) response (j=1 for TJC,j=2 for SJC, etc.), B is obtained using OLS, and S is the shrinkagematrix computed from the canonical co-ordinate system. Hence, thisapproach will yield sub-models corresponding to each component of DAS.

The fifth method (5) is Curds and Whey and Lasso in combination(CW-Lasso). Instead of using OLS to obtain B as in CW, Lasso was used,and the parameters were adjusted accordingly for the Lasso approach.

The performance of the five regression methods was compared in 70/30cross validation (repeatedly training in a randomly selected 70% of thedata and testing in the remaining 30%). The number of markers in eachregression model was chosen by using nested 10-fold cross-validationonce the number of markers was selected for a given analysis method thebest-fitting model of that size was used to represent the method. In theCW approaches (methods 4 and 5), nested 10 fold cross validation wasused for each sub-model corresponding to each component of DAS. Themodels developed using the CW-Lasso method performed best overall. Thefollowing sections consist of results mainly using CW-Lasso approach.

The 20 candidate biomarkers examined in all training samples wereprioritized according to a number of criteria, including: strength ofassociation with disease activity and contribution to multivariatemodels; consistency of correlation with disease activity acrossfeasibility and training data sets; CRP was excluded from any sub-modelsfor TJC, SJC, and PGA both because it is included in the DAS28-CRP4 andbecause it did not increase sub-model prediction accuracy in independenttest samples (CRP is used, however, in the final MBDA score calculationas part of the MBDA formula); robust assay performance (IL1B wasexcluded from final modeling because its concentrations too frequentlyfall below the limits of detection of immunoassays); known drug effects(IL6R was excluded from final modeling because it is known to bestrongly affected by tocilizumab, independently of the effects of thedrug on disease activity); and, stability (IL8 was excluded from finalmodeling because its measurable levels are known to rise dramaticallywhen serum samples are not kept cold). These criteria led to 15candidate biomarkers being considered for inclusion in the finalalgorithm. See Table 1.

TABLE 1 Biomarker Functional Category calprotectin cytokines andreceptors CHI3L1 skeletal EGF growth factors ICAM1 adhesion moleculesIL1RA cytokines and receptors IL6 cytokines and receptors LEP hormonesMMP1 matrix metalloproteinases MMP3 matrix metalloproteinases PYDskeletal RETN hormones SAA1 acute phase response TNFRSF1A cytokines andreceptors VCAM1 adhesion molecules VEGFA growth factors

Training the Algorithm

While all data was used in prioritizing biomarkers, a subset was usedfor training the final algorithm. This subset was selected to have abroad range of disease activity levels, so that patients at all levelsof disease activity were well represented. A comparison was made of theperformance of models trained using: only BRASS samples (167 total);BRASS samples plus InFoRM samples (167+˜100) selected to have a uniformdisease activity distribution; or, BRASS samples plus InFoRM samples(167+˜100) with a disease activity distribution like that of the BRASSsamples.

The model performance was evaluated in an independent set of BRASS andInFoRM samples (70 total) set aside for this purpose. The DAS28-CRPdistribution of this independent test set was similar to that of paststudies (approximately normal). As shown below, correlation (r) to theDAS28-CRP and area under the ROC curve (AUROC) for predicting high andlow DAS using median cut off were higher when training used BRASSsamples plus “BRASS-like” InFoRM samples, although the differences werenot statistically significant. The following Table 2 uses the Lassoregression method.

TABLE 2 Training Set r AUROC BRASS only 0.53 0.68 BRASS + Uniform InFoRM0.54 0.69 BRASS + BRASS-like InFoRM 0.55 0.71

For final training, the combination of BRASS plus “BRASS-like” InFoRMsamples was selected. The CW-Lasso regression method was chosen fordevelopment of the final algorithm because of its superior performancein cross validation within the training set and in testing using InFoRM512 patients and CAMERA patients (see below, MBDA algorithm performance,for a description of algorithm testing in another cohort of samples). Inthe application of this method, the shrinkage matrix was applied to thepredictions of TJC and SJC. Ten-fold cross-validation indicated that thefollowing 13 markers were optimal for performance. See Table 3.

TABLE 3 Marker TJC SJC PGA calprotectin X CHI3L1 X X EGF X X X IL6 X X XLEP X X MMP1 X MMP3 X PYD X X RETN X SAA1 X X X TNFRSF1A X X VCAM1 X XVEGF1 X X

From this set, PYD and calprotectin were excluded due to elevated assayfailure rates. The remaining 11 biomarkers gave very similar algorithmperformance to the full set of 13. An algorithm was chosen forvalidation that was developed by CW-Lasso regression using this11-marker to estimate the DAS28-CRP in data from the BRASS+BRASS-likeInFoRM samples. The estimates of TJC, SJC and PGHA were combined with aCRP test result in a formula similar to that used to calculate theDAS28-CRP.

${{DAS}\; 28\; {CRP}} = {{0.56\sqrt{TJC}} + {0.28\sqrt{SJC}} + {0.14\; {PGHA}} + {0.36\; {\log \left( {\frac{CRP}{10^{6}} + 1} \right)}} + 0.96}$${PDAS} = {{0.56\sqrt{IPTJC}} + {0.28\sqrt{IPSJC}} + {0.14\; {PPGHA}} + {0.36\; {\log \left( {\frac{CRP}{10^{6}} + 1} \right)}} + 0.96}$

Here IPTJC=Improved Prediction of TJC, IPSJC=Improved Prediction of SJC,PPGHA=Predicted PGHA, and PDAS is Predicted DAS28-CRP. (Details aredefined below; see Selected algorithm). The MBDA score is the resultfrom this formula.

Table 4 demonstrates the correlation of the values predicted by the PDASalgorithm with actual values for TJC, SJC, PGHA and DAS28-CRP, in thetwo cohorts studied, CAMERA and InFoRM.

TABLE 4 Study TJC SJC PGHA DA528-CRP CAMERA 0.445 0.536 0.427 0.726InFoRM 0.223 0.328 0.388 0.53 (512 subjects)

Selected Algorithm

The 11-marker+CRP Lasso model selected from the training process is asfollows:

PTJC=−38.564+3.997*(SAA1)^(1/10)+17.331*(IL6)^(1/10)+4.665*(CHI3L1)^(1/10)−15.236*(EGF)^(1/10)+2.651*(TNFRSF1A)^(1/10)+2.641*(LEP)^(1/10)+4.026*(VEGFA)^(1/10)−1.47*(VCAM1)^(1/10);

PSJC=−25.444+4.051*(SAA1)^(1/10)+16.154*(IL6)^(1/10)−11.847*(EGF)^(1/10)+3.091*(CHI3L1)^(1/10)+0.353*(TNFRSF1A)^(1/10);

PPGHA=−13.489+5.474*(IL6)^(1/10)+0.486*(SAA1)^(1/10)+2.246*(MMP1)^(1/10)+1.684*(leptin)^(1/10)+4.14*(TNFRSF1A)^(1/10)+2.292*(VEGFA)^(1/10)−1.898*(EGF)^(1/10)+0.028*(MMP3)^(1/10)−2.892*(VCAM1)^(1/10)−0.506*(RETN)^(1/10);

IPTJC=max(0.1739*PTJC+0.7865*PSJC,0);

IPSJC=max(0.1734*PTJC+0.7839*PSJC,0);

MBDA scoreround(max(min((0.56*sqrt(IPTJC)+0.28*sqrt(IPSJC)+0.14*PPGA+0.36*ln(CRP10⁶+1))*10.53+1,100),1)).

For the final DA algorithm, the results from the 11-marker+CRP CW-Lassomodel were scaled and rounded to be integers on a scale of 1-100 suchthat a MBDA score of 1 would be equivalent to a DAS28-CRP value of 0,and a MBDA score of 100 would be equivalent to a DAS28-CRP value of 9.4.

Gene names in the above formulas correspond to serum proteinconcentrations, as obtained by the MSD® platform. Biomarkerconcentrations were obtained in the ranges shown in Table 5 (95%interval).

TABLE 5 pg/ml Biomarker Lower Limit Upper Limit IL6 2.2 104 EGF 20 383VEGFA 83 776 LEP 2,226 139,885 SAA1 636,889 99,758,140 VCAM1 354,0261,054,681 CRP 245,332 76,399,801 MMP1 3,047 39,373 MMP3 9,203 134,262TNFRSF1A 1,139 4,532 RETN 3,635 19,308 CHI3L1 25,874 442,177

MBDA Algorithm Performance

In order to independently test the performance of the algorithmdeveloped above in this Example, a total of 120 serum samples wereanalyzed, obtained from the CAMERA study. Samples were obtained from theComputer Assisted Management in Early Rheumatoid Arthritis Study(CAMERA). From 1999-2003, all early rheumatoid arthritis patients (i.e.,disease duration of one year or less) who fulfilled the 1987 revisedAmerican College of Rheumatology (ACR) criteria for rheumatoid arthritiswere asked to participate in this two-year randomized, open-labelprospective multicentre strategy trial. As a result, 299 patients werestudied. Patients visited the outpatient clinic of one of the sixrheumatology departments in the region of Utrecht, the Netherlands,collaborating in the Utrecht Rheumatoid Arthritis Cohort study group.Inclusion criteria were that patients must have exhibited symptoms forless than one year, with age greater than 16 years. Exclusion criteriawere the previous use of glucocorticoids or any DMARD, use of cytotoxicor immunosuppressive drugs within a period of three months beforeinclusion, alcohol abuse, defined as more than two units per day, andpsychological problems, which would make adherence to the study protocolimpossible. At baseline all patients were monitored for medicalconditions that would interfere with MTX usage. This screening includeda chest X-ray, liver enzymes, albumin, hepatitis serology, serumcreatinine and complete blood count. An independent person performedrandomization in blocks of nine per hospital. The medical ethicscommittees of all participating hospitals approved this study, and allpatients gave written informed consent before entering the study.

The cohort for this study had the following characteristics: 69% female,68% CCP positive, 74% RF positive, 100% on MTX, 100% on non-biologicDMARDs, and 0% on biologic DMARDs. Additionally, the mean age of thecohort was 52 years (standard deviation (SD)+/−14.7), with a minimum ageof 17 and a maximum age of 78. The mean DAS28-CRP for this cohort was5.0 (SD+/−1.9), with a minimum of 0.9 and a maximum of 8.4.

A subpopulation of 72 subjects was selected from the CAMERA cohort forthis Example. All 72 patients were represented by baseline (time 0)visits and samples, and 48 were also represented by six-month visits andsamples. Within the visits selected, a wide distribution of DAS28-CRPscores were represented, ranging from a minimum of 0.96 to a maximum of8.4.

Of these, 72 samples were taken from subject baseline visits, and 48were from visits six months subsequent to baseline. The concentrationsof 23 serum protein biomarkers were measured in each sample: APOA1,APOC3, calprotectin, CCL22, CHI3L1 (YKL40), CRP, EGF, ICAM1, IL18, IL1B,IL1RA, IL6, IL6R, IL8, LEP, MMP1, MMP3, PYD, REIN, SAA1, TNFRSF1A,VCAM1, and VEGFA. The concentrations of the markers were determined bycustomized immunoassays using either the Meso Scale Discovery SECTOR®Imager 6000 or individual ELISAs.

The associations between individual biomarkers and the clinicalassessment measurements of DAS28-CRP, SJC28 and TJC28 were assessed byPearson correlation (r) for log-transformed concentrations. Thecorrelation p-values were adjusted for multiple hypothesis testing byestimating false discovery rates (FDR) using the method of Benjamin andHochberg. See J. Royal Stat. Soc. B 1995 57(1):289-300.

Of the 23 proteins examined, fourteen were statistically significantlycorrelated with DAS28-CRP, eleven with SJC28 and nine with TJC28(FDR<0.05). See Table 6, which shows the Pearson correlations (r)between individual biomarkers and each clinical disease activitymeasure. The q-values reflect the FDRs, and were calculated by adjustingthe p-values for multiple hypothesis testing. Statistically significantassociations (q<0.05) are in bold. As Table 6 shows, the individualbiomarkers associated with disease activity represented a range ofpathways associated with RA disease pathophysiology (FunctionalCategory).

TABLE 6 DAS28-CRP SJC28 TJC28 Biomarker Functional Category r q-val rq-val r q-val calprotectin cytokines and receptors 0.56 <0.01 0.38 <0.010.33 <0.01 CHI3L1 Skeletal 0.42 <0.01 0.35 <0.01 0.30 <0.01 CCL22cytokines and receptors −0.04 0.75 −0.13 0.19 −0.03 0.73 CRP acute phaseresponse 0.69 <0.01 0.41 <0.01 0.36 <0.01 EGF growth factors −0.07 0.46−0.08 0.42 −0.12 0.28 ICAM1 adhesion molecules 0.23 0.02 0.13 0.20 0.080.44 IL1B cytokines and receptors 0.45 <0.01 0.34 <0.01 0.31 <0.01 IL6cytokines and receptors 0.69 <0.01 0.50 <0.01 0.41 <0.01 IL6R cytokinesand receptors 0.01 0.97 0.03 0.71 0.02 0.89 IL8 cytokines and receptors0.47 <0.01 0.46 <0.01 0.30 <0.01 IL1RA cytokines and receptors 0.01 0.970.05 0.58 −0.09 0.44 LEP hormones 0.00 0.97 −0.07 0.53 −0.06 0.56 MMP1MMPs 0.36 <0.01 0.29 <0.01 0.19 0.06 MMP3 MMPs 0.51 <0.01 0.40 <0.010.26 <0.01 PYD skeletal 0.23 0.04 0.29 <0.01 0.21 0.09 RETN hormones0.22 0.03 0.13 0.20 0.13 0.28 SAA1 acute phase response 0.66 <0.01 0.43<0.01 0.37 <0.01 TNFRSF1A cytokines and receptors 0.36 <0.01 0.30 <0.010.24 0.02 VCAM1 adhesion molecules 0.13 0.24 0.14 0.20 0.08 0.56 VEGFAgrowth factors 0.29 <0.01 0.18 0.12 0.07 0.56

Two pre-specified algorithms, a prototype and a final algorithm, usingsubsets of these 23 biomarkers were applied to calculate a total MBDAscore for each subject at each visit (baseline and six-month). Thesealgorithms were trained in prior studies using independent samples fromother clinical cohorts. Algorithm performance was evaluated by Pearsoncorrelation (r) and area under the ROC curve (AUROC) for identifyinghigh and low disease activity at the baseline and six-month visits. Thereference classification for ROC analysis was based on a DAS28-CRP of2.67, the threshold separating remission/low disease activity frommoderate and high disease activity.

Prototype Algorithm for Multivariate Model

The first algorithm, or “prototype algorithm,” using a linearcombination of protein biomarkers, was trained on subject samples toestimate the DAS28 directly and was provided by the formula describedelsewhere herein according to:

MBDA=b ₀ +b ₁*DAIMRK₁ ^(x) −b ₂*DAIMRK₂ ^(x) −b ₃*DAIMRK₃ ^(x) . . . −b_(n)*DAIMRK_(n) ^(x);

where MBDA is the MBDA score, b_(0-n) are constants, and DAIMRK_(1-n)^(x) are the serum concentrations, transformed to the x^(th) power, of ndifferent biomarkers selected from the panel of biomarkers.

The prototype algorithm used in this Example was:

MBDA=(−16.1564)−(0.0606*Calprotectin^(1/10))+(0.2194*CHI3L1^(1/10))+(1.1886*ICAM^(1/10))+(2.7738*IL6^(1/10))+(0.7254*MMP1^(1/10))−(0.8348*MMP3^(1/10))+(1.0296*PYD^(1/10))+(1.1792*SAA1^(1/10))+(2.4422*TNFRSF1A^(1/10))+(0.3272*VEGFA^(1/10)).

The prototype algorithm achieved a Pearson correlation (r) of 0.65 andan AUROC of 0.84 relative to the DAS28-CRP.

Biomarker Selection for Final Algorithm

The second algorithm was derived using serum biomarker concentrations toseparately estimate the three clinical assessments of TJC28, SJC28 andPGHA. Note that all of these are components of the formula used incalculating DAS28-CRP:

DAS28-CRP=0.56*sqrt(TJC28)+0.28*sqrt(SJC28)+0.36*ln(CRP+1)+(0.014*PGHA)+0.96.

Biomarkers were then selected to predict and estimate clinicalassessments of disease activity, specifically PGHA, TJC28 and SJC28. Theresulting estimates were combined with a serum CRP concentrationmeasurement to calculate an overall MBDA score. See FIG. 1, whichindicates the three panels of biomarkers predictive of clinical diseaseactivity measurements, the union thereof, and CRP. The CW-Lasso methodwas used to predict the individual components of the DAS28; i.e., TJC28,SJC28 and PGHA. Note that biomarker terms are included in the CW-Lassoif they help to improve cross-validated model performance, and thiscriterion does not imply that each term is statistically significant byunivariate analysis. A biomarker could make a significant contributionto a multivariate model even if it does not have a significantunivariate correlation, and could not make a significant contribution toa multivariate model even though it has a significant univariatecorrelation. Indeed, a comparison of each algorithm predictive for aclinical assessment, (a)-(c) above, with the biomarkers of Table 3 showsthat not all biomarkers in each algorithm were individuallystatistically correlated with that clinical assessment. For example,values for serum concentrations of EGF, LEP, VEGFA and VCAM1 are allincluded in the algorithm for predicting TJC28, yet each of thesemarkers individually demonstrated a q-value for correlation with TJC of≥0.28. Including these markers, however, improves multivariate modelperformance in independent cross-validation test sets.

The overall MBDA score derived according to the methods of the presentExample was given as a whole number between 1 and 100. The formula usedto derive this score was provided by:

MBDAScore=((0.56*sqrt(PTJC)+0.28*sqrt(PSJC)+0.36*log(CRP/10⁶+1)+(0.14*PPGHA)+0.96)*10.53)+1,

where PTJC=predicted TJC28, PSJC=predicted SJC28, and PPGHA=predictedPGA. This examples includes data from the following set of biomarkers:SAA1, IL6, CHI3L1, EGF, TNFRSF1A, LEP, VEGFA and VCAM1 for PTJC; SAA1,IL6, EGF, CHI3L1 and TNFRSF1A for PSJC; SAA1, MMP1, LEP, TNFRSF1A,VEGFA, EGF, MMP3, VCAM1 and RETN for PPGHA; plus CRP. In total,therefore, data from the following set of 12 markers was used to derivea MBDA score: CHI3L1, CRP, EGF, IL6, LEP, MMP1, MMP3, RETN, SAA1,TNFRSF1A, VCAM1 and VEGFA. The predicted clinical assessments of diseaseactivity were developed according to the following formulas:

PTJC=−38.564+(3.997*SAA1^(1/10))+(17.331*IL6^(1/10))+(4.665*CHI3L1^(1/10))−(15.236*EGFI^(1/10))+(2.651*TNFRSF1A^(1/10))+(2.641*LEP^(1/10))+(4.026*VEGFA^(1/10))−(1.47*VCAM^(1/10));  (a)

PSJC=−25.444+(4.051*SAA1^(1/10))+(16.154*IL6^(1/10))−(11.847*EGF^(1/10))+(3.091*CHI3L1^(1/10))+(0.353*TNFRSF1A^(1/10));and,  (b)

PPGHA=−13.489+(5.474*IL6^(1/10))+(0.486*SAA1^(1/10))+(2.246*MMP1^(1/10))+(1.684*LEP^(1/10))+(4.14*TNFRSF1A^(1/10))+(2.292*VEGFA^(1/10))−(1.898*EGF^(1/10))+(0.028*MMP3^(1/10))−(2.892*VCAM1^(1/10))−(0.506*RETN^(1/10)).  (c)

The performance of the above algorithm in deriving a MBDA score wasevaluated by Pearson correlation (r) and area under the ROC curve(AUROC) for identifying high and low disease activity at the baselineand six-month visits. The Pearson correlation was 0.73, and the AUROCwas 0.87, with the reference classification for ROC analysis based on athreshold DAS28-CRP of 2.67, the threshold separating remission/lowdisease activity from moderate and high disease activity. The changes inbiomarker-based MBDA scores between the baseline and six-month visitswere assessed by the paired Wilcoxon rank sum test.

To ensure that performance of the second algorithm was not overestimateddue to the inclusion of two samples for some patients, subsets ofsamples were also analyzed that included only one randomly selectedvisit for each subject. The algorithm performed equally well in thesesubsets. Possible bias in the AUROC due to an imbalance in numbersbetween low and high disease activity groups was also analyzed using aDAS28-CRP cutoff of 2.67. When the cutoff was set at the medianDAS28-CRP of 4.6, the AUROC was 0.83.

When the predictions of the individual components of the DAS28 generatedby the MBDA algorithm were correlated to the actual TJC28, SJC28 andPGHA, the correlation coefficients were seen to trend higher (and thusprovide better correlation with clinical disease activity measurements)than the coefficients for CRP, a marker commonly used alone as anindicator of RA disease activity. See FIG. 1.

An analysis was then done to determine whether the MBDA score changed inresponse to the treatment protocols used in the CAMERA study. For allsubjects for whom MBDA Scores were available for both visits (baselineand six-month), the median score dropped from 52 to 37 (p=2.2E-6; n=46).The intensive and conventional treatment arms were consideredseparately. There was also a significant decrease in median MBDA Scorein the intensive treatment arm, from 52 to 36 (p=2.5E-5; n=31). In theconventional treatment arm, the median MBDA Score decreased from 59 to45 (p=0.06; n=15).

In conclusion, this Example demonstrates that serum protein biomarkersrepresenting a variety of biological pathways were consistentlyassociated with RA disease activity. A pre-specified MBDA algorithmcombining information from several of these biomarkers performed well inpredicting RA disease activity when evaluated in an independent testset. The algorithm's estimates of TJC, SJC and PGHA correlated to actualclinical measures of disease activity. Furthermore, subsequent MBDAscores of the subjects analyzed decreased compared to initial MBDAscores following and in response to treatment.

Example 2: Use of MBDA Score to Predict Disease Relapse, IncludingFlare, in Patients with Rheumatoid Arthritis Who Withdrawal from Therapy

Prediction of which rheumatoid arthritis (RA) patients in low diseaseactivity (LDA) can successfully discontinue therapy can improve thecost-effectiveness of RA management. This example demonstrates that achange in the multi-biomarker disease activity (MBDA) score can predictdisease relapse after RA therapy discontinuation. The MBDA scoredescribed in this example is an MBDA score derived from the 12 biomarkerVECTRA® DA panel as disclosed in Table 5 of Example 1 above.

Methods

Data were used from 439 RA patients who were randomized to stopTNF-alpha inhibition (TNFi) treatment in the Dutch multi-center POETtrial. Table 7 provides the baseline characteristics of the POET studypopulation. All patients had been in DAS28<3.2 (LDA) for ≥12 months. Inthe study TNFi was allowed to be restarted if RA relapsed according toreimbursement criteria: DAS28 exceeding 3.2 again, but patients and/orphysicians were allowed, if DAS28 increase was minor, to escalate thedose off the conventional disease modifiers. In the current analysis 3definitions of relapse were assessed during the 12 months from TNFidiscontinuation: 1) re-initiating TNFi treatment, 2) escalation of anymedication and 3) physician-reported flare. MBDA score, which measuresRA disease activity on a scale of 1 to 100 with validated levels of low(<30), moderate (30 to 44) and high (>44), was assessed at baseline.Associations between baseline MBDA score and each definition of diseaserelapse by 12 months post-TNFi discontinuation were evaluated usingunivariate analysis and multivariate logistic regression, adjusted forpotential confounders.

TABLE 7 Total Continue Stop Characteristic (N = 664) (n = 225) (n = 439)P Female, % 66.4 64.3 67.4 0.418 Age, years 59. (10.7)  59.2 (10.4)  59.8 (10.8)   0.440 Disease  9 (5-14)  9 (5-14) 10 (6-17) 0.159duration, years BMI 26.0 (4.4) 26.1 (4.6)   25.9 (4.3)   0.617 RFpositive, % 67.7 68.4 67.3 0.789 Anti-CCP 69.0 68.9 69.1 0.957 positive% Erosive disease, 61.3 58.5 62.8 0.293 % ESR 10.5 (5-19)   10.5(5-19)    9 (5-17) 0.436 CRP 2 (1-4) 2 (1-4) 2 (1-5) 0.129 TJC28 0 (0-0)0 (0-0) 0 (0-1) 0.159 SJC28 0 (0-0) 0 (0-0) 1 (0-0) 0.008 PGA 20.6(10-26)  20.6 (10-26)    20.7 (9.0-28.1) 0.789 DAS28-ESR 2.0 (0.8)   2.1(0.7)   2.0 (0.8)   0.161 MBDA score 30.5 (12.5)   31.1 (12.5)   30.2(12.6)   0.352 Number of 0.988 TNFi, % 1^(st) 86.4 86.2 86.5 2^(nd) 11.511.6 11.4 3^(rd)  2.1  2.2  2.1 DMARD, (%) 0.146 Methotrexate 582(87.8)  200 (88.9)  382 (87.2)  Other 52 (7.8)  17 (7.6)  50 (11.4)cDMARD No DMARD 29 (4.4)  7 (3.1) 7 (1.6) Values are mean (standarddeviation), median (interquartile range) or %. Differences betweengroups are tested with independent samples t-tests and median tests fornormally and non-normally disturbed continuous variables, respectively,and Pearson chi-square tests for categorical variables. TNFi = tumornecrosis factor-alpha inhibitors; DAS28 = Disease Activity Score in 28joints; BMI = Body Mass Index; RF = Rheumatoid Factor; anti-CCP =anti-cyclic citrullinated peptide; ESR = erythrocyte sedimentation rate;CR = C-reactive protein; TJC28 = 28-joint tender count; SJC28 = 28-jointswollen joint count; PGA = patient global assessment; DMARD = DiseaseModifying Anti-Rhuematic Drug; cDMARD = conventional DMARD; MBDA =Multi-biomarker disease activity.

Statistical Analysis

Descriptive statistics were computed for the baseline demographic anddisease-related characteristics of the 439 included patients. Besidesre-initiating TNFi, any medication escalation and physician-reportedflare were used as criteria for relapse. Medication escalation wasdefined as re-initiating TNFi or starting or increasing any biologicalor non-biological DMARD (including corticosteroids). Baselinecharacteristics of patients that did and did not re-initiate TNFitreatment within 12 months were compared using independent samplest-tests and median tests for normally and non-normally distributedcontinuous variables and Pearson χ2 tests for categorical variables.Differences in the proportions of patients meeting the differentcriteria for between patients with low (<30), moderate (30-44) and high(>44) MBDA scores were compared by univariate Pearson χ2 tests. Patientswho dropped out before 12 months without relapse were counted in thisanalysis with those who continued to have a response. Next, relapse-freesurvival was examined for the three MBDA score groups using Kaplan-Meiersurvival curves. In this analysis, patients who dropped out earlywithout relapse were censored at the time of withdrawal. Between-groupdifferences in survival were tested by log-rank χ2 tests. Based on theresults of the univariate and survival analyses, MBDA scores werefurther dichotomized as high vs. moderate/low. Finally, univariate andmultivariable logistic regression analyses were performed to calculateunadjusted odds ratios (ORs) for the risk of relapse associated with ahigh MBDA score, ORs adjusted for baseline DAS28 scores, and ORs furtheradjusted for all significant (P<0.05) confounding baseline differencesbetween those who did and did not re-initiate TNFi treatment. Allanalyses were performed using SPSS, version 22.

Results

At baseline, 50.1%, 35.3% and 14.6% of patients had low, moderate orhigh MBDA scores and 94.1%, 5.9%, 0% had low, moderate high DAS28 (seeTable 8).

TABLE 8 Low (<30) Moderate (30-44) High (>44) MBDA/DAS28 N = 220 N = 155N = 64 DAS28 Remission 189 119 41 (<2.6) DAS28 Low (2.6-3.2) 25 26 13DAS28 Moderate 6 10 10 (3.2-5.1) DAS28 High (>5.1) 0 0 0

Within 12 months, 49.9% of patients who discontinued TNFi treatment atbaseline had restarted TNFi medication, 59.0% had escalation of anymedication and 57.2% had experienced at least one physician-reportedflare (Table 9).

TABLE 9 Relapse criterion Stop Continue TNFi re-initiation 219 (49.9%) 6(2.7%) Medication escalation 259 (59.0%) 27 (12.0%) Clinician-reportedflare 251 (57.2%) 18 (8.0%) 

MBDA scores at baseline were predictive for each definition of relapse.At least one definition of relapse was observed by 12 months in 59.5%,68.4% and 81.3% of patients with low, moderate, or high MBDA score atbaseline, respectively (P=0.004) (Table 10). Adjusted for baselineDAS28-ESR, disease duration, BMI and erosions, high MBDA scores (>44)were associated with an increased risk for TNFi re-initiation (OR=1.85,95% CI 1.00-3.40), medication escalation (OR=1.99, 95% CI 1.01-3.94) andphysician-reported flare (OR=2.00, 95% CI 1.06-3.77). Table 10demonstrates the occurrence of relapse by four definitions at 12 monthsfor patients classified by baseline MBDA score, which is furtherillustrated using Kaplan-Meier survival curves (FIG. 2).

TABLE 10 Moderate Relapse Low (<30) (30-44) High (>44) definition TotalN = 220 N = 155 N = 64 P TNFi 219 102 (46.4%) 74 (47.7%) 43 (67.2%)0.011 re-initiation Medication 259 117 (53.2%) 92 (59.4%) 50 (78.1%)0.002 escalation Clinician- 251 116 (52.7%) 87 (56.1%) 48 (75.0%) 0.006reported flare Any criterion 289 131 (59.5%) 106 (68.4%)  52 (81.3%)0.004 Any criterion = TNFi re-initiation, medication escalation orclinician-reported flare. P-value by Pearson χ2 test.

Table 11 demonstrates the occurrence of relapse by four definitions at12 months for patients classified by baseline MBDA score excluding 26patients with DAS28≥3.2 at baseline.

TABLE 11 Moderate Relapse Low (<30) (30-44) High (>44) definition TotalN = 220 N = 155 N = 64 P TNFi 205  99 (46.3%) 69 (47.6%) 37 (68.5%)0.012 re-initiation Medication 240 113 (52.8%) 86 (59.3%) 41 (75.9%)0.008 escalation Clinician- 236 113 (52.8%) 82 (56.6%) 41 (75.9%) 0.009reported flare Any criterion 269 127 (59.3%) 99 (68.3%) 43 (79.6%) 0.012Any criterion = TNFi re-initiation, medication escalation orclinician-reported flare. P-value by Pearson χ2 test.

Table 12 demonstrates the overlap in patients meeting the differentcriteria of relapse within 12 months in the stop group.

TABLE 12 TNFi re- Medication Relapse initiation escalation criterion NOYES NO YES TNFi re- initiation No Yes Medication escalation No 180 0 Yes40 219 Clinician- reported flare No 176 12 150 38 Yes 44 207 30 221

Table 13 demonstrates the univariate baseline associations with relapseat 12 months (P values) in the stop group.

TABLE 13 TNFi Medication Clinician- Combined Characteristic restartescalation reported flare relapse Female 0.735 0.452 0.799 0.976 Age0.990 0.242 0.169 0.300 Disease duration 0.001 0.024 0.011 0.012 BMI0.028 0.004 0.135 0.017 RF positive 0.530 0.340 0.865 0.564 Anti-CCP0.775 0.988 0.209 0.977 positive Erosive disease 0.020 0.074 0.194 0.060ESR 0.227 0.145 0.161 0.091 CRP 0.134 0.030 0.592 0.150 TJC28 0.2260.005 0.016 0.003 SJC28 0.316 0.007 0.254 0.011 PGA 0.164 0.143 0.1490.050 DAS28-ESR 0.073 0.001 0.017 <0.0001 MBDA score 0.008 <0.001 0.002<0.0001 Number of TNFi 0.621 0.392 0.322 0.423 cDMARD 0.670 0.379 0.8520.494 P-values <0.10 are in bold (except for individual DAS28-ESRcomponents and CRP). Combined relapse = TNFi re-initiation, medicationsescalation, clinician-reported flare or DAS28 flare.

Table 14 demonstrates univariate and multivariable regression analysesof high (>44) versus moderate or low baseline MBDA score as a predictorof relapse in the stop group.

TABLE 14 Relapse Relapse at 6 months Relapse at 12 months criterion OR(95% CI) P OR (95% CI) P TNFi re- initiation Unadjusted 1.74 (1.02-2.96)0.042 2.32 (1.32-4.05) 0.003 Adjusted 1.61 (0.94-2.78) 0.085 2.17(1.23-3.83) 0.008 Fully adjusted 1.43 (0.80-2.55) 0.228 1.85 (1.00-3.40)0.049 Medication escalation Unadjusted 1.97 (1.14-3.39) 0.015 2.84(1.52-5.31) 0.001 Adjusted 1.76 (1.01-3.08) 0.046 2.44 (1.29-4.62) 0.006Fully adjusted 1.51 (0.84)   0.168 1.99 (1.01-3.94) 0.047 Clinician-reported flare Unadjusted 2.06 (1.19-3.57) 0.010 2.54 (1.39-4.64) 0.02Adjusted 1.86 (1.06-3.27) 0.029 2.31 (1.25-4.25) 0.007 Fully adjusted1.69 (0.94-3.05) 0.082 2.00 (1.06-3.77) 0.033 Combined relapseUnadjusted 1.81 (1.03-3.17) 0.038 2.52 (1.30-4.89) 0.012 Adjusted 1.59(0.90-2.82) 0.112 2.12 (1.08-4.16) 0.029 Fully adjusted 1.40 (0.76-2.56)0.277 1.68 (0.83-3.40) 0.147 Adjusted = Adjusted for baseline DAS28-ESRscores; Fully adjusted = Adjusted for baseline DAS28-ESR (continuous),disease duration (continuous), BMI (continuous) and erosions (yes/no).Combined relapse = TNFi re-initiation, medications escalation,clinician-reported flare or DAS28 flare.

Table 15 demonstrates univariate and multivariable regression analysesof high (>44) versus moderate or low baseline MBDA score as a predictorof relapse in the continuation group.

TABLE 15 Relapse Relapse at 6 months Relapse at 12 months criterion OR(95% CI) P OR (95% CI) P TNFi re- initiation Unadjusted 0.00 0.998 0.000.998 Adjusted 0.00 0.998 0.00 0.998 Fully adjusted 0.00 0.998 0.000.998 Medication escalation Unadjusted 0.00 0.998 0.76 (0.21-2.69) 0.669Adjusted 0.00 0.998 1.08 (0.29-4.03) 0.913 Fully adjusted 0.00 0.9980.98 (0.25-3.78) 0.974 Clinician- reported flare Unadjusted 1.84(0.36-9.30) 0.460 0.77 (0.17-3.51) 0.733 Adjusted 1.17 (0.21-6.47) 0.8540.63 (1.13-3.04) 0.567 Fully adjusted 0.45 (0.06-3.14) 0.420 0.50(1.10-2.52) 0.397 Combined relapse Unadjusted 0.82 (0.18-3.79) 0.8030.90 (0.32-2.53) 0.849 Adjusted 0.72 (0.15-3.48) 0.681 1.02 (0.35-2.95)0.976 Fully adjusted 0.50 (0.10-2.62) 0.416 0.87 (0.29-2.61) 0.806Adjusted = Adjusted for baseline DAS28-ESR scores; Fully adjusted =Adjusted for baseline DAS28-ESR (continuous), disease duration(continuous), BMI (continuous) and erosions (yes/no). Combined relapse =TNFi re-initiation, medications escalation, clinician-reported flare orDAS28 flare.

CONCLUSION

This example shows that, for RA patients in remission or stable lowdisease activity, a high MBDA score at the time of TNFi discontinuationwas significantly associated with disease relapse during the next 12months. Over 80% of patients with a high baseline MBDA score relapsedaccording to at least one of the three criteria used. This rate ofrelapse was up to twice as great as for patients with low or moderateMBDA scores, suggesting that patients with low clinical disease activityand high MBDA scores may have inflammation that is partly or entirelysubclinical. This example thus shows that the MBDA score was a predictorof relapse independently of DAS28-ESR, which suggests that MBDA is be aclinically useful tool for identifying patients who are at increasedrisk of unsuccessful TNFi discontinuation.

In this example, higher BMI scores were univariately associated withincreased odds of meeting two criteria of disease relapse but notphysician-reported flare, and longer disease duration was a strongpredictor for all three definitions of disease relapse. Erosive diseasewas univariately associated with TNFi restart. Neither positivity for RFnor ACPA was associated with disease relapse. This example provides thefirst results to demonstrate the utility of the MBDA score as apredictor for risk of disease relapse in RA patients who discontinuedTNFi treatment at baseline.

This example showing that MBDA score is a predictor of relapse risk isstrengthened by having used 3 different definitions of diseaserelapse: 1) restarting TNFi treatment, 2) escalation of any DMARDtherapy and 3) physician-reported flare, which identified more relapsesthan with any one criterion alone. MBDA scores at baseline werepredictive of each definition of disease relapse.

In conclusion, for RA patients in remission or stable low diseaseactivity, a high baseline MBDA score was frequently observed and wasfound to be an independent predictor of disease relapse within 12 monthsof TNFi discontinuation. These results suggest that the MBDA score is aclinically useful tool for identifying subgroups of patients who have anincreased risk of relapse when stopping TNFi treatment.

All publications and patent applications cited in this specification areherein incorporated by reference as if each individual publication orpatent application were specifically and individually indicated to beincorporated by reference.

Although the foregoing invention has been described in some detail byway of illustration and example for purposes of clarity ofunderstanding, it will be readily apparent to one of ordinary skill inthe art in light of the teachings of this invention that certain changesand modifications may be made thereto without departing from the spiritor scope of the invention as defined in the appended claims.

What is claimed is:
 1. A method for recommending a therapeutic regimenin a subject having an autoimmune disorder, the method comprising: a)administering a therapeutic regimen to the subject; b) performing animmunoassay on a sample from the subject to generate a score based on aset of quantitative data, wherein the set of quantitative data comprisesexpression data for at least four biomarkers, wherein the at least fourbiomarkers comprise at least four markers selected from chitinase 3-like1 (cartilage glycoprotein-39) (CHI3L1); C-reactive protein,pentraxin-related (CRP); epidermal growth factor (beta-urogastrone)(EGF); interleukin 6 (interferon, beta 2) (IL6); leptin (LEP); matrixmetallopeptidase 1 (interstitial collagenase) (MMP1); matrixmetallopeptidase 3 (stromelysin 1, progelatinase) (MMP3); resistin(RETN); serum amyloid A1 (SAA1); tumor necrosis factor receptorsuperfamily, member 1A (TNFRSF1A); vascular cell adhesion molecule 1(VCAM1); and, vascular endothelial growth factor A (VEGFA); and c)recommending i) withdrawal from the therapeutic regimen if the score islow or moderate; or ii) no withdrawal from the therapeutic regimen ifthe score is high.
 2. The method of claim 1, wherein the at least fourbiomarkers comprise IL6, EGF, SAA1, and CRP.
 3. The method of claim 1,wherein the at least four biomarkers comprise IL6, EGF, VEGFA, LEP,SAA1, VCAM1, CRP, MMP1, MMP3, TNFRSF1A, RETN, and CHI3L1.
 4. The methodof claim 1, wherein performance of the immunoassay comprises: obtainingthe sample, wherein the sample comprises the protein markers; contactingthe sample with a plurality of distinct reagents; generating a pluralityof distinct complexes between the reagents and markers; and detectingthe complexes to generate the data.
 5. The method of claim 1, whereinthe immunoassay comprises a multiplex assay.
 6. The method of claim 1,wherein the therapeutic regimen prevents radiographic progression. 7.The method of claim 1, wherein the therapeutic regimen prevents relapse.8. The method of claim 1, wherein the autoimmune disorder is anarthritic disorder.
 9. The method of claim 8, wherein the arthriticdisorder is rheumatoid arthritis.
 10. The method of claim 9, wherein therheumatoid arthritis is early rheumatoid arthritis.
 11. The method ofclaim 1, wherein the score is on a scale of 1-100, wherein is score islow if the score is <30, wherein the score is moderate if the score is30-44, and wherein the score is high if the score is >44.
 12. The methodof claim 1, wherein the therapeutic regimen is a disease modifyinganti-rheumatoid drug (DMARD).
 13. The method of claim 12, wherein theDMARD therapeutic regimen comprises one or more of MTX, sulfasalazine(SSZ), or hydroxychloroquine (HCQ).
 14. The method of claim 1, whereinthe therapeutic regimen is a biologic therapeutic regimen.
 15. Themethod of claim 14, wherein the biologic therapeutic regimen comprises aTNF inhibitor.
 16. The method of claim 15, wherein the TNF inhibitor isinfliximab.
 17. The method of claim 1, wherein the score is predictiveof a clinical assessment.
 18. The method of claim 17, wherein theclinical assessment is selected from the group consisting of: a DAS, aDAS28, a DAS28-CRP, a DAS28-ESR, a Sharp score, a tender joint count(TJC), and a swollen joint count (SJC).
 19. The method of claim 7wherein relapse is indicated by restarting therapy, escalation oftherapy, or flare.
 20. The method of claim 19 wherein the flare isphysician-reported flare. 21-40. (canceled)